{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Network Traffic Forecasting with _AutoTS_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In telco, accurate forecast of KPIs (e.g. network traffic, utilizations, user experience, etc.) for communication networks ( 2G/3G/4G/5G/wired) can help predict network failures, allocate resource, or save energy. \n",
    "\n",
    "In this notebook, we demostrate a reference use case where we use the network traffic KPI(s) in the past to predict traffic KPI(s) in the future. We demostrate how to use `AutoTS` in project [Zouwu](https://github.com/intel-analytics/analytics-zoo/tree/master/pyzoo/zoo/zouwu) to do time series forecasting in an automated and distributed way.\n",
    "\n",
    "For demonstration, we use the publicly available network traffic data repository maintained by the [WIDE project](http://mawi.wide.ad.jp/mawi/) and in particular, the network traffic traces aggregated every 2 hours (i.e. AverageRate in Mbps/Gbps and Total Bytes) in year 2018 and 2019 at the transit link of WIDE to the upstream ISP ([dataset link](http://mawi.wide.ad.jp/~agurim/dataset/)). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Helper functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This section defines some helper functions to be used in the following procedures. You can refer to it later when they're used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:42:29.365279Z",
     "start_time": "2020-04-05T09:42:29.360456Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_drop_dates_and_len(df, allow_missing_num=3):\n",
    "    \"\"\"\n",
    "    Find missing values and get records to drop\n",
    "    \"\"\"\n",
    "    missing_num = df.total.isnull().astype(int).groupby(df.total.notnull().astype(int).cumsum()).sum()\n",
    "    drop_missing_num = missing_num[missing_num > allow_missing_num]\n",
    "    drop_datetimes = df.iloc[drop_missing_num.index].index\n",
    "    drop_len = drop_missing_num.values\n",
    "    return drop_datetimes, drop_len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:42:29.567920Z",
     "start_time": "2020-04-05T09:42:29.561650Z"
    }
   },
   "outputs": [],
   "source": [
    "def rm_missing_weeks(start_dts, missing_lens, df):\n",
    "    \"\"\"\n",
    "    Drop weeks that contains more than 3 consecutive missing values.\n",
    "    If consecutive missing values across weeks, we remove all the weeks.\n",
    "    \"\"\" \n",
    "    for start_time, l in zip(start_dts, missing_lens):\n",
    "        start = start_time - pd.Timedelta(days=start_time.dayofweek)\n",
    "        start = start.replace(hour=0, minute=0, second=0)\n",
    "        start_week_end = start + pd.Timedelta(days=6)\n",
    "        start_week_end = start_week_end.replace(hour=22, minute=0, second=0)\n",
    "\n",
    "        end_time = start_time + l*pd.Timedelta(hours=2)\n",
    "        if start_week_end < end_time:\n",
    "            end = end_time + pd.Timedelta(days=6-end_time.dayofweek)\n",
    "            end = end.replace(hour=22, minute=0, second=0)\n",
    "        else:\n",
    "            end = start_week_end\n",
    "        df = df.drop(df[start:end].index)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:42:29.752013Z",
     "start_time": "2020-04-05T09:42:29.746678Z"
    }
   },
   "outputs": [],
   "source": [
    "# plot the predicted values and actual values (for the test data)\n",
    "def plot_result(test_df, pred_df, dt_col=\"datetime\", value_col=\"AvgRate\", look_back=1):\n",
    "    # target column of dataframe is \"value\"\n",
    "    # past sequence length is 50\n",
    "    pred_value = pred_df[value_col].values\n",
    "    true_value = test_df[value_col].values[look_back:]\n",
    "    fig, axs = plt.subplots(figsize=(12, 5))\n",
    "\n",
    "    axs.plot(pred_df[dt_col], pred_value, color='red', label='predicted values')\n",
    "    axs.plot(test_df[dt_col][look_back:], true_value, color='blue', label='actual values')\n",
    "    axs.set_title('the predicted values and actual values (for the test data)')\n",
    "\n",
    "    plt.xlabel(dt_col)\n",
    "    plt.xticks(rotation=45)\n",
    "    plt.ylabel(value_col)\n",
    "    plt.legend(loc='upper left')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download raw dataset and load into dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we download the dataset and load it into a pandas dataframe. Steps are as below. \n",
    "\n",
    "* First, run the script `get_data.sh` to download the raw data. It will download the monthly aggregated traffic data in year 2018 and 2019 into `data` folder. The raw data contains aggregated network traffic (average MBPs and total bytes) as well as other metrics. \n",
    "\n",
    "* Second, run `extract_data.sh` to extract relavant traffic KPI's from raw data, i.e. `AvgRate` for average use rate, and `total` for total bytes. The script will extract the KPI's with timestamps into `data/data.csv`.\n",
    "\n",
    "* Finally, use pandas to load `data/data.csv` into a dataframe as shown below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:42:30.934219Z",
     "start_time": "2020-04-05T09:42:30.324968Z"
    }
   },
   "outputs": [],
   "source": [
    "import os \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:42:30.948562Z",
     "start_time": "2020-04-05T09:42:30.935369Z"
    }
   },
   "outputs": [],
   "source": [
    "raw_df = pd.read_csv(\"data/data.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below are some example records of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:42:31.060336Z",
     "start_time": "2020-04-05T09:42:30.949794Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>StartTime</th>\n",
       "      <th>EndTime</th>\n",
       "      <th>AvgRate</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018/01/01 00:00:00</td>\n",
       "      <td>2018/01/01 02:00:00</td>\n",
       "      <td>306.23Mbps</td>\n",
       "      <td>275605455598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018/01/01 02:00:00</td>\n",
       "      <td>2018/01/01 04:00:00</td>\n",
       "      <td>285.03Mbps</td>\n",
       "      <td>256527692256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018/01/01 04:00:00</td>\n",
       "      <td>2018/01/01 06:00:00</td>\n",
       "      <td>247.39Mbps</td>\n",
       "      <td>222652190823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018/01/01 06:00:00</td>\n",
       "      <td>2018/01/01 08:00:00</td>\n",
       "      <td>211.55Mbps</td>\n",
       "      <td>190396029658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018/01/01 08:00:00</td>\n",
       "      <td>2018/01/01 10:00:00</td>\n",
       "      <td>234.82Mbps</td>\n",
       "      <td>211340468977</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             StartTime              EndTime     AvgRate         total\n",
       "0  2018/01/01 00:00:00  2018/01/01 02:00:00  306.23Mbps  275605455598\n",
       "1  2018/01/01 02:00:00  2018/01/01 04:00:00  285.03Mbps  256527692256\n",
       "2  2018/01/01 04:00:00  2018/01/01 06:00:00  247.39Mbps  222652190823\n",
       "3  2018/01/01 06:00:00  2018/01/01 08:00:00  211.55Mbps  190396029658\n",
       "4  2018/01/01 08:00:00  2018/01/01 10:00:00  234.82Mbps  211340468977"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data pre-processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we need to do data cleaning and preprocessing on the raw data. Note that this part could vary for different dataset. \n",
    "\n",
    "For the network traffic data we're using, the processing contains 3 parts:\n",
    "1. Convert string datetime to TimeStamp\n",
    "2. Unify the measurement scale for `AvgRate` value - some uses Mbps, some uses Gbps \n",
    "3. Handle missing data (fill or drop)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:42:31.439100Z",
     "start_time": "2020-04-05T09:42:31.433819Z"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(pd.to_datetime(raw_df.StartTime))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:42:31.619691Z",
     "start_time": "2020-04-05T09:42:31.614336Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Mbps', 'Gbps'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can find 'AvgRate' is of two scales: 'Mbps' and 'Gbps' \n",
    "raw_df.AvgRate.str[-4:].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:42:31.817366Z",
     "start_time": "2020-04-05T09:42:31.810257Z"
    }
   },
   "outputs": [],
   "source": [
    "# Unify AvgRate value\n",
    "df['AvgRate'] = raw_df.AvgRate.apply(lambda x: float(x[:-4]) if x.endswith(\"Mbps\") else float(x[:-4]) * 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:42:31.989842Z",
     "start_time": "2020-04-05T09:42:31.986685Z"
    }
   },
   "outputs": [],
   "source": [
    "df[\"total\"] = raw_df[\"total\"]\n",
    "df.set_index(\"StartTime\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:42:32.169108Z",
     "start_time": "2020-04-05T09:42:32.163918Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no. of n/a values:\n",
      "AvgRate    3\n",
      "total      3\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "full_idx = pd.date_range(start=df.index.min(), end=df.index.max(), freq='2H')\n",
    "df = df.reindex(full_idx)\n",
    "print(\"no. of n/a values:\")\n",
    "print(df.isna().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we drop weeks with more than 3 consecutive missing values and fill other missing values remained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:42:32.545450Z",
     "start_time": "2020-04-05T09:42:32.534666Z"
    }
   },
   "outputs": [],
   "source": [
    "drop_dts, drop_len = get_drop_dates_and_len(df)\n",
    "df = rm_missing_weeks(drop_dts, drop_len, df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:42:32.715750Z",
     "start_time": "2020-04-05T09:42:32.713641Z"
    }
   },
   "outputs": [],
   "source": [
    "df.ffill(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:42:32.917402Z",
     "start_time": "2020-04-05T09:42:32.913676Z"
    }
   },
   "outputs": [],
   "source": [
    "# AutoTS requires input data frame with a datetime column\n",
    "df.index.name = \"datetime\"\n",
    "df = df.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:42:33.110253Z",
     "start_time": "2020-04-05T09:42:33.101643Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>AvgRate</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-01 00:00:00</td>\n",
       "      <td>306.23</td>\n",
       "      <td>2.756055e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-01 02:00:00</td>\n",
       "      <td>285.03</td>\n",
       "      <td>2.565277e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-01 04:00:00</td>\n",
       "      <td>247.39</td>\n",
       "      <td>2.226522e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-01 06:00:00</td>\n",
       "      <td>211.55</td>\n",
       "      <td>1.903960e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-01 08:00:00</td>\n",
       "      <td>234.82</td>\n",
       "      <td>2.113405e+11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             datetime  AvgRate         total\n",
       "0 2018-01-01 00:00:00   306.23  2.756055e+11\n",
       "1 2018-01-01 02:00:00   285.03  2.565277e+11\n",
       "2 2018-01-01 04:00:00   247.39  2.226522e+11\n",
       "3 2018-01-01 06:00:00   211.55  1.903960e+11\n",
       "4 2018-01-01 08:00:00   234.82  2.113405e+11"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:42:33.292662Z",
     "start_time": "2020-04-05T09:42:33.280539Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AvgRate</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8760.000000</td>\n",
       "      <td>8.760000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>454.050030</td>\n",
       "      <td>4.084920e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>238.377074</td>\n",
       "      <td>2.146655e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>86.490000</td>\n",
       "      <td>8.641890e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>273.715000</td>\n",
       "      <td>2.459111e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>410.590000</td>\n",
       "      <td>3.694360e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>558.200000</td>\n",
       "      <td>5.023054e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1760.000000</td>\n",
       "      <td>1.585238e+12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           AvgRate         total\n",
       "count  8760.000000  8.760000e+03\n",
       "mean    454.050030  4.084920e+11\n",
       "std     238.377074  2.146655e+11\n",
       "min      86.490000  8.641890e+09\n",
       "25%     273.715000  2.459111e+11\n",
       "50%     410.590000  3.694360e+11\n",
       "75%     558.200000  5.023054e+11\n",
       "max    1760.000000  1.585238e+12"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the data to see how the KPI's look like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:42:33.826495Z",
     "start_time": "2020-04-05T09:42:33.641397Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df.plot(y='AvgRate',figsize=(12,5), title=\"AvgRate of network traffic data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:42:33.971770Z",
     "start_time": "2020-04-05T09:42:33.827516Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df.plot(y='total',figsize=(12,5), title=\"total bytes of network traffic data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time series forecasting with _AutoTS_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_AutoTS_ provides AutoML support for building end-to-end time series analysis pipelines (including automatic feature generation, model selection and hyperparameter tuning).\n",
    "\n",
    "The general workflow using automated training contains below two steps. \n",
    "   1. create a ```AutoTSTrainer``` to train a ```TSPipeline```, save it to file to use later or elsewhere if you wish.\n",
    "   2. use ```TSPipeline``` to do prediction, evaluation, and incremental fitting as well. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, you need to initialize RayOnSpark before using auto training (i.e. ```AutoTSTrainer```), and stop it after training finished. (Note RayOnSpark is not needed if you just use `TSPipeline` for inference, evaluation or incremental training.)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:42:51.917822Z",
     "start_time": "2020-04-05T09:42:34.581511Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/shane/.local/lib/python3.6/site-packages/bigdl/util/engine.py:41: UserWarning: Find both SPARK_HOME and pyspark. You may need to check whether they match with each other. SPARK_HOME environment variable is set to: /home/shane/shane/spark, and pyspark is found in: /home/shane/anaconda3/envs/automl/lib/python3.6/site-packages/pyspark/__init__.py. If they are unmatched, please use one source only to avoid conflict. For example, you can unset SPARK_HOME and use pyspark only.\n",
      "  warnings.warn(warning_msg)\n",
      "/home/shane/anaconda3/envs/automl/lib/python3.6/site-packages/zoo/util/engine.py:42: UserWarning: Find both SPARK_HOME and pyspark. You may need to check whether they match with each other. SPARK_HOME environment variable is set to: /home/shane/shane/spark, and pyspark is found in: /home/shane/anaconda3/envs/automl/lib/python3.6/site-packages/pyspark/__init__.py. If they are unmatched, you are recommended to use one source only to avoid conflict. For example, you can unset SPARK_HOME and use pyspark only.\n",
      "  warnings.warn(warning_msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prepending /home/shane/.local/lib/python3.6/site-packages/bigdl/share/conf/spark-bigdl.conf to sys.path\n",
      "Adding /home/shane/anaconda3/envs/automl/lib/python3.6/site-packages/zoo/share/lib/analytics-zoo-bigdl_0.10.0-spark_2.4.3-0.8.0-SNAPSHOT-jar-with-dependencies.jar to BIGDL_JARS\n",
      "Prepending /home/shane/anaconda3/envs/automl/lib/python3.6/site-packages/zoo/share/conf/spark-analytics-zoo.conf to sys.path\n",
      "Current pyspark location is : /home/shane/anaconda3/envs/automl/lib/python3.6/site-packages/pyspark/__init__.py\n",
      "Start to getOrCreate SparkContext\n",
      "Successfully got a SparkContext\n",
      "Start to launch the JVM guarding process\n",
      "JVM guarding process has been successfully launched\n",
      "Start to launch ray on cluster\n",
      "Start to launch ray on local\n"
     ]
    }
   ],
   "source": [
    "# init RayOnSpark in local mode\n",
    "from zoo import init_spark_on_local\n",
    "from zoo.ray import RayContext\n",
    "sc = init_spark_on_local(cores=4, spark_log_level=\"INFO\")\n",
    "ray_ctx = RayContext(sc=sc, object_store_memory=\"1g\")\n",
    "ray_ctx.init()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we initialize a `AutoTSTrainer`.\n",
    "* `dt_col`: the column specifying datetime.\n",
    "* `target_col`: target column to predict. Here, we take `AvgRate` KPI as an example.\n",
    "* `horizon` : num of steps to look forward. \n",
    "* `extra_feature_col`: a list of columns which are also included in input data frame as features except target column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:42:53.144776Z",
     "start_time": "2020-04-05T09:42:51.919760Z"
    }
   },
   "outputs": [],
   "source": [
    "from zoo.zouwu.autots.forecast import AutoTSTrainer\n",
    "\n",
    "trainer = AutoTSTrainer(dt_col=\"datetime\",\n",
    "                        target_col=\"AvgRate\",\n",
    "                        horizon=1,\n",
    "                        extra_features_col=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can set some searching presets such as `look_back` which indicates the history time period we want to use for forecasting.\n",
    "lookback can be an int which it is a fixed values, or can be a tuple to indicate the range for sampling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:44:10.228166Z",
     "start_time": "2020-04-05T09:44:10.225074Z"
    }
   },
   "outputs": [],
   "source": [
    "# look back in range from one week to 3 days to predict the next 2h.\n",
    "look_back = (36, 84)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to split the data frame into train, validation and test data frame before training. You can use `train_val_test_split` as an easy way to finish it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:44:56.311570Z",
     "start_time": "2020-04-05T09:44:56.298100Z"
    }
   },
   "outputs": [],
   "source": [
    "from zoo.automl.common.util import train_val_test_split\n",
    "train_df, val_df, test_df = train_val_test_split(df, \n",
    "                                                 val_ratio=0.1, \n",
    "                                                 test_ratio=0.1,\n",
    "                                                 look_back=look_back[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we fit on train data and validation data. \n",
    "\n",
    "You can use `recipe` to specify searching method as well as other searching presets such as stop criteria .etc. The `GridRandomRecipe` here is a recipe that combines grid search with random search to find the best set of parameters. For more details, please refer to analytics-zoo document [here](https://analytics-zoo.github.io/master/#APIGuide/AutoML/recipe/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:44:59.067809Z",
     "start_time": "2020-04-05T09:44:59.062375Z"
    }
   },
   "outputs": [],
   "source": [
    "from zoo.automl.config.recipe import LSTMGridRandomRecipe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:48:03.575923Z",
     "start_time": "2020-04-05T09:45:31.368537Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-04-05 17:45:31,476\tINFO tune.py:60 -- Tip: to resume incomplete experiments, pass resume='prompt' or resume=True to run()\n",
      "2020-04-05 17:45:31,477\tINFO tune.py:223 -- Starting a new experiment.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "== Status ==\n",
      "Using FIFO scheduling algorithm.\n",
      "Resources requested: 0/4 CPUs, 0/0 GPUs (0/1.0 ps, 0/1.0 trainer)\n",
      "Memory usage on this node: 2.2/16.6 GB\n",
      "\n",
      "WARNING:tensorflow:From /home/shane/anaconda3/envs/automl/lib/python3.6/site-packages/ray/tune/logger.py:127: The name tf.VERSION is deprecated. Please use tf.version.VERSION instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/shane/anaconda3/envs/automl/lib/python3.6/site-packages/ray/tune/logger.py:132: The name tf.summary.FileWriter is deprecated. Please use tf.compat.v1.summary.FileWriter instead.\n",
      "\n",
      "== Status ==\n",
      "Using FIFO scheduling algorithm.\n",
      "Resources requested: 2/4 CPUs, 0/0 GPUs (0/1.0 ps, 0/1.0 trainer)\n",
      "Memory usage on this node: 2.2/16.6 GB\n",
      "Result logdir: /home/shane/ray_results/automl\n",
      "Number of trials: 3 ({'RUNNING': 1, 'PENDING': 2})\n",
      "PENDING trials:\n",
      " - train_func_1_batch_size=64,dropout_2=0.41106,lr=0.0094198,lstm_1_units=128,lstm_2_units=32,past_seq_len=74,selected_features=['WEEKDAY(datetime)' 'IS_WEEKEND(datetime)' 'MONTH(datetime)'\n",
      " 'IS_AWAKE(datetime)' 'HOUR(datetime)' 'DAY(datetime)']:\tPENDING\n",
      " - train_func_2_batch_size=64,dropout_2=0.25682,lr=0.009007,lstm_1_units=16,lstm_2_units=64,past_seq_len=40,selected_features=['WEEKDAY(datetime)' 'DAY(datetime)' 'IS_AWAKE(datetime)'\n",
      " 'MONTH(datetime)']:\tPENDING\n",
      "RUNNING trials:\n",
      " - train_func_0_batch_size=64,dropout_2=0.39015,lr=0.0055474,lstm_1_units=16,lstm_2_units=16,past_seq_len=73,selected_features=['DAY(datetime)' 'HOUR(datetime)' 'IS_WEEKEND(datetime)'\n",
      " 'IS_BUSY_HOURS(datetime)' 'WEEKDAY(datetime)']:\tRUNNING\n",
      "\n",
      "\u001b[2m\u001b[36m(pid=9119)\u001b[0m Prepending /home/shane/.local/lib/python3.6/site-packages/bigdl/share/conf/spark-bigdl.conf to sys.path\n",
      "\u001b[2m\u001b[36m(pid=9119)\u001b[0m Prepending /home/shane/anaconda3/envs/automl/lib/python3.6/site-packages/zoo/share/conf/spark-analytics-zoo.conf to sys.path\n",
      "\u001b[2m\u001b[36m(pid=9119)\u001b[0m Prepending /home/shane/.local/lib/python3.6/site-packages/bigdl/share/conf/spark-bigdl.conf to sys.path\n",
      "\u001b[2m\u001b[36m(pid=9119)\u001b[0m Prepending /home/shane/anaconda3/envs/automl/lib/python3.6/site-packages/zoo/share/conf/spark-analytics-zoo.conf to sys.path\n",
      "\u001b[2m\u001b[36m(pid=9118)\u001b[0m Prepending /home/shane/.local/lib/python3.6/site-packages/bigdl/share/conf/spark-bigdl.conf to sys.path\n",
      "\u001b[2m\u001b[36m(pid=9118)\u001b[0m Prepending /home/shane/anaconda3/envs/automl/lib/python3.6/site-packages/zoo/share/conf/spark-analytics-zoo.conf to sys.path\n",
      "\u001b[2m\u001b[36m(pid=9118)\u001b[0m Prepending /home/shane/.local/lib/python3.6/site-packages/bigdl/share/conf/spark-bigdl.conf to sys.path\n",
      "\u001b[2m\u001b[36m(pid=9118)\u001b[0m Prepending /home/shane/anaconda3/envs/automl/lib/python3.6/site-packages/zoo/share/conf/spark-analytics-zoo.conf to sys.path\n",
      "\u001b[2m\u001b[36m(pid=9118)\u001b[0m /home/shane/.local/lib/python3.6/site-packages/bigdl/util/engine.py:41: UserWarning: Find both SPARK_HOME and pyspark. You may need to check whether they match with each other. SPARK_HOME environment variable is set to: /home/shane/shane/spark, and pyspark is found in: /home/shane/anaconda3/envs/automl/lib/python3.6/site-packages/pyspark/__init__.py. If they are unmatched, please use one source only to avoid conflict. For example, you can unset SPARK_HOME and use pyspark only.\n",
      "\u001b[2m\u001b[36m(pid=9118)\u001b[0m   warnings.warn(warning_msg)\n",
      "\u001b[2m\u001b[36m(pid=9118)\u001b[0m /home/shane/anaconda3/envs/automl/lib/python3.6/site-packages/zoo/util/engine.py:42: UserWarning: Find both SPARK_HOME and pyspark. You may need to check whether they match with each other. SPARK_HOME environment variable is set to: /home/shane/shane/spark, and pyspark is found in: /home/shane/anaconda3/envs/automl/lib/python3.6/site-packages/pyspark/__init__.py. If they are unmatched, you are recommended to use one source only to avoid conflict. For example, you can unset SPARK_HOME and use pyspark only.\n",
      "\u001b[2m\u001b[36m(pid=9118)\u001b[0m   warnings.warn(warning_msg)\n",
      "\u001b[2m\u001b[36m(pid=9183)\u001b[0m Prepending /home/shane/.local/lib/python3.6/site-packages/bigdl/share/conf/spark-bigdl.conf to sys.path\n",
      "\u001b[2m\u001b[36m(pid=9183)\u001b[0m Prepending /home/shane/anaconda3/envs/automl/lib/python3.6/site-packages/zoo/share/conf/spark-analytics-zoo.conf to sys.path\n",
      "\u001b[2m\u001b[36m(pid=9119)\u001b[0m /home/shane/.local/lib/python3.6/site-packages/bigdl/util/engine.py:41: UserWarning: Find both SPARK_HOME and pyspark. You may need to check whether they match with each other. SPARK_HOME environment variable is set to: /home/shane/shane/spark, and pyspark is found in: /home/shane/anaconda3/envs/automl/lib/python3.6/site-packages/pyspark/__init__.py. If they are unmatched, please use one source only to avoid conflict. For example, you can unset SPARK_HOME and use pyspark only.\n",
      "\u001b[2m\u001b[36m(pid=9119)\u001b[0m   warnings.warn(warning_msg)\n",
      "\u001b[2m\u001b[36m(pid=9119)\u001b[0m /home/shane/anaconda3/envs/automl/lib/python3.6/site-packages/zoo/util/engine.py:42: UserWarning: Find both SPARK_HOME and pyspark. You may need to check whether they match with each other. SPARK_HOME environment variable is set to: /home/shane/shane/spark, and pyspark is found in: /home/shane/anaconda3/envs/automl/lib/python3.6/site-packages/pyspark/__init__.py. If they are unmatched, you are recommended to use one source only to avoid conflict. For example, you can unset SPARK_HOME and use pyspark only.\n",
      "\u001b[2m\u001b[36m(pid=9119)\u001b[0m   warnings.warn(warning_msg)\n",
      "\u001b[2m\u001b[36m(pid=9184)\u001b[0m Prepending /home/shane/.local/lib/python3.6/site-packages/bigdl/share/conf/spark-bigdl.conf to sys.path\n",
      "\u001b[2m\u001b[36m(pid=9184)\u001b[0m Prepending /home/shane/anaconda3/envs/automl/lib/python3.6/site-packages/zoo/share/conf/spark-analytics-zoo.conf to sys.path\n",
      "\u001b[2m\u001b[36m(pid=9183)\u001b[0m Prepending /home/shane/.local/lib/python3.6/site-packages/bigdl/share/conf/spark-bigdl.conf to sys.path\n",
      "\u001b[2m\u001b[36m(pid=9183)\u001b[0m Prepending /home/shane/anaconda3/envs/automl/lib/python3.6/site-packages/zoo/share/conf/spark-analytics-zoo.conf to sys.path\n",
      "\u001b[2m\u001b[36m(pid=9183)\u001b[0m /home/shane/.local/lib/python3.6/site-packages/bigdl/util/engine.py:41: UserWarning: Find both SPARK_HOME and pyspark. You may need to check whether they match with each other. SPARK_HOME environment variable is set to: /home/shane/shane/spark, and pyspark is found in: /home/shane/anaconda3/envs/automl/lib/python3.6/site-packages/pyspark/__init__.py. If they are unmatched, please use one source only to avoid conflict. For example, you can unset SPARK_HOME and use pyspark only.\n",
      "\u001b[2m\u001b[36m(pid=9183)\u001b[0m   warnings.warn(warning_msg)\n",
      "\u001b[2m\u001b[36m(pid=9183)\u001b[0m /home/shane/anaconda3/envs/automl/lib/python3.6/site-packages/zoo/util/engine.py:42: UserWarning: Find both SPARK_HOME and pyspark. You may need to check whether they match with each other. SPARK_HOME environment variable is set to: /home/shane/shane/spark, and pyspark is found in: /home/shane/anaconda3/envs/automl/lib/python3.6/site-packages/pyspark/__init__.py. If they are unmatched, you are recommended to use one source only to avoid conflict. For example, you can unset SPARK_HOME and use pyspark only.\n",
      "\u001b[2m\u001b[36m(pid=9183)\u001b[0m   warnings.warn(warning_msg)\n",
      "\u001b[2m\u001b[36m(pid=9184)\u001b[0m Prepending /home/shane/.local/lib/python3.6/site-packages/bigdl/share/conf/spark-bigdl.conf to sys.path\n",
      "\u001b[2m\u001b[36m(pid=9184)\u001b[0m Prepending /home/shane/anaconda3/envs/automl/lib/python3.6/site-packages/zoo/share/conf/spark-analytics-zoo.conf to sys.path\n",
      "\u001b[2m\u001b[36m(pid=9118)\u001b[0m /home/shane/.local/lib/python3.6/site-packages/bigdl/util/engine.py:41: UserWarning: Find both SPARK_HOME and pyspark. You may need to check whether they match with each other. SPARK_HOME environment variable is set to: /home/shane/shane/spark, and pyspark is found in: /home/shane/anaconda3/envs/automl/lib/python3.6/site-packages/pyspark/__init__.py. If they are unmatched, please use one source only to avoid conflict. For example, you can unset SPARK_HOME and use pyspark only.\n",
      "\u001b[2m\u001b[36m(pid=9118)\u001b[0m   warnings.warn(warning_msg)\n",
      "\u001b[2m\u001b[36m(pid=9118)\u001b[0m /home/shane/anaconda3/envs/automl/lib/python3.6/site-packages/zoo/util/engine.py:42: UserWarning: Find both SPARK_HOME and pyspark. You may need to check whether they match with each other. SPARK_HOME environment variable is set to: /home/shane/shane/spark, and pyspark is found in: /home/shane/anaconda3/envs/automl/lib/python3.6/site-packages/pyspark/__init__.py. If they are unmatched, you are recommended to use one source only to avoid conflict. For example, you can unset SPARK_HOME and use pyspark only.\n",
      "\u001b[2m\u001b[36m(pid=9118)\u001b[0m   warnings.warn(warning_msg)\n",
      "\u001b[2m\u001b[36m(pid=9183)\u001b[0m /home/shane/.local/lib/python3.6/site-packages/bigdl/util/engine.py:41: UserWarning: Find both SPARK_HOME and pyspark. You may need to check whether they match with each other. SPARK_HOME environment variable is set to: /home/shane/shane/spark, and pyspark is found in: /home/shane/anaconda3/envs/automl/lib/python3.6/site-packages/pyspark/__init__.py. If they are unmatched, please use one source only to avoid conflict. For example, you can unset SPARK_HOME and use pyspark only.\n",
      "\u001b[2m\u001b[36m(pid=9183)\u001b[0m   warnings.warn(warning_msg)\n",
      "\u001b[2m\u001b[36m(pid=9183)\u001b[0m /home/shane/anaconda3/envs/automl/lib/python3.6/site-packages/zoo/util/engine.py:42: UserWarning: Find both SPARK_HOME and pyspark. You may need to check whether they match with each other. SPARK_HOME environment variable is set to: /home/shane/shane/spark, and pyspark is found in: /home/shane/anaconda3/envs/automl/lib/python3.6/site-packages/pyspark/__init__.py. If they are unmatched, you are recommended to use one source only to avoid conflict. For example, you can unset SPARK_HOME and use pyspark only.\n",
      "\u001b[2m\u001b[36m(pid=9183)\u001b[0m   warnings.warn(warning_msg)\n",
      "\u001b[2m\u001b[36m(pid=9184)\u001b[0m /home/shane/.local/lib/python3.6/site-packages/bigdl/util/engine.py:41: UserWarning: Find both SPARK_HOME and pyspark. You may need to check whether they match with each other. SPARK_HOME environment variable is set to: /home/shane/shane/spark, and pyspark is found in: /home/shane/anaconda3/envs/automl/lib/python3.6/site-packages/pyspark/__init__.py. If they are unmatched, please use one source only to avoid conflict. For example, you can unset SPARK_HOME and use pyspark only.\n",
      "\u001b[2m\u001b[36m(pid=9184)\u001b[0m   warnings.warn(warning_msg)\n",
      "\u001b[2m\u001b[36m(pid=9184)\u001b[0m /home/shane/anaconda3/envs/automl/lib/python3.6/site-packages/zoo/util/engine.py:42: UserWarning: Find both SPARK_HOME and pyspark. You may need to check whether they match with each other. SPARK_HOME environment variable is set to: /home/shane/shane/spark, and pyspark is found in: /home/shane/anaconda3/envs/automl/lib/python3.6/site-packages/pyspark/__init__.py. If they are unmatched, you are recommended to use one source only to avoid conflict. For example, you can unset SPARK_HOME and use pyspark only.\n",
      "\u001b[2m\u001b[36m(pid=9184)\u001b[0m   warnings.warn(warning_msg)\n",
      "\u001b[2m\u001b[36m(pid=9184)\u001b[0m /home/shane/.local/lib/python3.6/site-packages/bigdl/util/engine.py:41: UserWarning: Find both SPARK_HOME and pyspark. You may need to check whether they match with each other. SPARK_HOME environment variable is set to: /home/shane/shane/spark, and pyspark is found in: /home/shane/anaconda3/envs/automl/lib/python3.6/site-packages/pyspark/__init__.py. If they are unmatched, please use one source only to avoid conflict. For example, you can unset SPARK_HOME and use pyspark only.\n",
      "\u001b[2m\u001b[36m(pid=9184)\u001b[0m   warnings.warn(warning_msg)\n",
      "\u001b[2m\u001b[36m(pid=9184)\u001b[0m /home/shane/anaconda3/envs/automl/lib/python3.6/site-packages/zoo/util/engine.py:42: UserWarning: Find both SPARK_HOME and pyspark. You may need to check whether they match with each other. SPARK_HOME environment variable is set to: /home/shane/shane/spark, and pyspark is found in: /home/shane/anaconda3/envs/automl/lib/python3.6/site-packages/pyspark/__init__.py. If they are unmatched, you are recommended to use one source only to avoid conflict. For example, you can unset SPARK_HOME and use pyspark only.\n",
      "\u001b[2m\u001b[36m(pid=9184)\u001b[0m   warnings.warn(warning_msg)\n",
      "\u001b[2m\u001b[36m(pid=9119)\u001b[0m /home/shane/.local/lib/python3.6/site-packages/bigdl/util/engine.py:41: UserWarning: Find both SPARK_HOME and pyspark. You may need to check whether they match with each other. SPARK_HOME environment variable is set to: /home/shane/shane/spark, and pyspark is found in: /home/shane/anaconda3/envs/automl/lib/python3.6/site-packages/pyspark/__init__.py. If they are unmatched, please use one source only to avoid conflict. For example, you can unset SPARK_HOME and use pyspark only.\n",
      "\u001b[2m\u001b[36m(pid=9119)\u001b[0m   warnings.warn(warning_msg)\n",
      "\u001b[2m\u001b[36m(pid=9119)\u001b[0m /home/shane/anaconda3/envs/automl/lib/python3.6/site-packages/zoo/util/engine.py:42: UserWarning: Find both SPARK_HOME and pyspark. You may need to check whether they match with each other. SPARK_HOME environment variable is set to: /home/shane/shane/spark, and pyspark is found in: /home/shane/anaconda3/envs/automl/lib/python3.6/site-packages/pyspark/__init__.py. If they are unmatched, you are recommended to use one source only to avoid conflict. For example, you can unset SPARK_HOME and use pyspark only.\n",
      "\u001b[2m\u001b[36m(pid=9119)\u001b[0m   warnings.warn(warning_msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2m\u001b[36m(pid=9184)\u001b[0m 2020-04-05 17:45:32,982\tWARNING worker.py:204 -- Calling ray.get or ray.wait in a separate thread may lead to deadlock if the main thread blocks on this thread and there are not enough resources to execute more tasks\n",
      "\u001b[2m\u001b[36m(pid=9184)\u001b[0m 2020-04-05 17:45:32,982\tWARNING worker.py:204 -- Calling ray.get or ray.wait in a separate thread may lead to deadlock if the main thread blocks on this thread and there are not enough resources to execute more tasks\n",
      "\u001b[2m\u001b[36m(pid=9184)\u001b[0m LSTM is selected.\n",
      "\u001b[2m\u001b[36m(pid=9184)\u001b[0m LSTM is selected.\n",
      "\u001b[2m\u001b[36m(pid=9119)\u001b[0m LSTM is selected.\n",
      "\u001b[2m\u001b[36m(pid=9119)\u001b[0m WARNING:tensorflow:From /home/shane/.local/lib/python3.6/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "\u001b[2m\u001b[36m(pid=9119)\u001b[0m Instructions for updating:\n",
      "\u001b[2m\u001b[36m(pid=9119)\u001b[0m If using Keras pass *_constraint arguments to layers.\n",
      "\u001b[2m\u001b[36m(pid=9184)\u001b[0m WARNING:tensorflow:From /home/shane/.local/lib/python3.6/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "\u001b[2m\u001b[36m(pid=9184)\u001b[0m Instructions for updating:\n",
      "\u001b[2m\u001b[36m(pid=9184)\u001b[0m If using Keras pass *_constraint arguments to layers.\n",
      "\u001b[2m\u001b[36m(pid=9119)\u001b[0m LSTM is selected.\n",
      "\u001b[2m\u001b[36m(pid=9184)\u001b[0m WARNING:tensorflow:From /home/shane/.local/lib/python3.6/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "\u001b[2m\u001b[36m(pid=9184)\u001b[0m Instructions for updating:\n",
      "\u001b[2m\u001b[36m(pid=9184)\u001b[0m If using Keras pass *_constraint arguments to layers.\n",
      "\u001b[2m\u001b[36m(pid=9119)\u001b[0m WARNING:tensorflow:From /home/shane/.local/lib/python3.6/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "\u001b[2m\u001b[36m(pid=9119)\u001b[0m Instructions for updating:\n",
      "\u001b[2m\u001b[36m(pid=9119)\u001b[0m If using Keras pass *_constraint arguments to layers.\n",
      "\u001b[2m\u001b[36m(pid=9184)\u001b[0m WARNING:tensorflow:From /home/shane/.local/lib/python3.6/site-packages/tensorflow_core/python/ops/math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "\u001b[2m\u001b[36m(pid=9184)\u001b[0m Instructions for updating:\n",
      "\u001b[2m\u001b[36m(pid=9184)\u001b[0m Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "\u001b[2m\u001b[36m(pid=9184)\u001b[0m WARNING:tensorflow:From /home/shane/.local/lib/python3.6/site-packages/tensorflow_core/python/ops/math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "\u001b[2m\u001b[36m(pid=9184)\u001b[0m Instructions for updating:\n",
      "\u001b[2m\u001b[36m(pid=9184)\u001b[0m Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "\u001b[2m\u001b[36m(pid=9119)\u001b[0m WARNING:tensorflow:From /home/shane/.local/lib/python3.6/site-packages/tensorflow_core/python/ops/math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "\u001b[2m\u001b[36m(pid=9119)\u001b[0m Instructions for updating:\n",
      "\u001b[2m\u001b[36m(pid=9119)\u001b[0m Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "\u001b[2m\u001b[36m(pid=9119)\u001b[0m WARNING:tensorflow:From /home/shane/.local/lib/python3.6/site-packages/tensorflow_core/python/ops/math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "\u001b[2m\u001b[36m(pid=9119)\u001b[0m Instructions for updating:\n",
      "\u001b[2m\u001b[36m(pid=9119)\u001b[0m Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "\u001b[2m\u001b[36m(pid=9184)\u001b[0m 2020-04-05 17:45:34.923735: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/shane/torch/install/lib:/home/shane/torch/install/lib:/home/shane/torch/install/lib:\n",
      "\u001b[2m\u001b[36m(pid=9184)\u001b[0m 2020-04-05 17:45:34.923761: E tensorflow/stream_executor/cuda/cuda_driver.cc:318] failed call to cuInit: UNKNOWN ERROR (303)\n",
      "\u001b[2m\u001b[36m(pid=9184)\u001b[0m 2020-04-05 17:45:34.923778: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (shane-workstation): /proc/driver/nvidia/version does not exist\n",
      "\u001b[2m\u001b[36m(pid=9184)\u001b[0m 2020-04-05 17:45:34.923945: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA\n",
      "\u001b[2m\u001b[36m(pid=9184)\u001b[0m 2020-04-05 17:45:34.923735: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/shane/torch/install/lib:/home/shane/torch/install/lib:/home/shane/torch/install/lib:\n",
      "\u001b[2m\u001b[36m(pid=9184)\u001b[0m 2020-04-05 17:45:34.923761: E tensorflow/stream_executor/cuda/cuda_driver.cc:318] failed call to cuInit: UNKNOWN ERROR (303)\n",
      "\u001b[2m\u001b[36m(pid=9184)\u001b[0m 2020-04-05 17:45:34.923778: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (shane-workstation): /proc/driver/nvidia/version does not exist\n",
      "\u001b[2m\u001b[36m(pid=9184)\u001b[0m 2020-04-05 17:45:34.923945: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA\n",
      "\u001b[2m\u001b[36m(pid=9119)\u001b[0m 2020-04-05 17:45:34.948973: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/shane/torch/install/lib:/home/shane/torch/install/lib:/home/shane/torch/install/lib:\n",
      "\u001b[2m\u001b[36m(pid=9119)\u001b[0m 2020-04-05 17:45:34.948990: E tensorflow/stream_executor/cuda/cuda_driver.cc:318] failed call to cuInit: UNKNOWN ERROR (303)\n",
      "\u001b[2m\u001b[36m(pid=9119)\u001b[0m 2020-04-05 17:45:34.949005: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (shane-workstation): /proc/driver/nvidia/version does not exist\n",
      "\u001b[2m\u001b[36m(pid=9119)\u001b[0m 2020-04-05 17:45:34.949176: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA\n",
      "\u001b[2m\u001b[36m(pid=9119)\u001b[0m 2020-04-05 17:45:34.953666: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 3591790000 Hz\n",
      "\u001b[2m\u001b[36m(pid=9119)\u001b[0m 2020-04-05 17:45:34.953915: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7efe5526d160 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n",
      "\u001b[2m\u001b[36m(pid=9119)\u001b[0m 2020-04-05 17:45:34.953928: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version\n",
      "\u001b[2m\u001b[36m(pid=9184)\u001b[0m 2020-04-05 17:45:34.947009: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 3591790000 Hz\n",
      "\u001b[2m\u001b[36m(pid=9184)\u001b[0m 2020-04-05 17:45:34.947372: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f0b612f2950 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n",
      "\u001b[2m\u001b[36m(pid=9184)\u001b[0m 2020-04-05 17:45:34.947394: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version\n",
      "\u001b[2m\u001b[36m(pid=9184)\u001b[0m 2020-04-05 17:45:34.947009: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 3591790000 Hz\n",
      "\u001b[2m\u001b[36m(pid=9184)\u001b[0m 2020-04-05 17:45:34.947372: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f0b612f2950 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n",
      "\u001b[2m\u001b[36m(pid=9184)\u001b[0m 2020-04-05 17:45:34.947394: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version\n",
      "\u001b[2m\u001b[36m(pid=9119)\u001b[0m 2020-04-05 17:45:34.948973: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/shane/torch/install/lib:/home/shane/torch/install/lib:/home/shane/torch/install/lib:\n",
      "\u001b[2m\u001b[36m(pid=9119)\u001b[0m 2020-04-05 17:45:34.948990: E tensorflow/stream_executor/cuda/cuda_driver.cc:318] failed call to cuInit: UNKNOWN ERROR (303)\n",
      "\u001b[2m\u001b[36m(pid=9119)\u001b[0m 2020-04-05 17:45:34.949005: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (shane-workstation): /proc/driver/nvidia/version does not exist\n",
      "\u001b[2m\u001b[36m(pid=9119)\u001b[0m 2020-04-05 17:45:34.949176: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA\n",
      "\u001b[2m\u001b[36m(pid=9119)\u001b[0m 2020-04-05 17:45:34.953666: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 3591790000 Hz\n",
      "\u001b[2m\u001b[36m(pid=9119)\u001b[0m 2020-04-05 17:45:34.953915: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7efe5526d160 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n",
      "\u001b[2m\u001b[36m(pid=9119)\u001b[0m 2020-04-05 17:45:34.953928: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/shane/anaconda3/envs/automl/lib/python3.6/site-packages/ray/tune/logger.py:110: The name tf.Summary is deprecated. Please use tf.compat.v1.Summary instead.\n",
      "\n",
      "== Status ==\n",
      "Using FIFO scheduling algorithm.\n",
      "Resources requested: 4/4 CPUs, 0/0 GPUs (0/1.0 ps, 0/1.0 trainer)\n",
      "Memory usage on this node: 3.0/16.6 GB\n",
      "Result logdir: /home/shane/ray_results/automl\n",
      "Number of trials: 3 ({'RUNNING': 2, 'PENDING': 1})\n",
      "PENDING trials:\n",
      " - train_func_2_batch_size=64,dropout_2=0.25682,lr=0.009007,lstm_1_units=16,lstm_2_units=64,past_seq_len=40,selected_features=['WEEKDAY(datetime)' 'DAY(datetime)' 'IS_AWAKE(datetime)'\n",
      " 'MONTH(datetime)']:\tPENDING\n",
      "RUNNING trials:\n",
      " - train_func_0_batch_size=64,dropout_2=0.39015,lr=0.0055474,lstm_1_units=16,lstm_2_units=16,past_seq_len=73,selected_features=['DAY(datetime)' 'HOUR(datetime)' 'IS_WEEKEND(datetime)'\n",
      " 'IS_BUSY_HOURS(datetime)' 'WEEKDAY(datetime)']:\tRUNNING, [2 CPUs, 0 GPUs], [pid=9119], 13 s, 1 iter\n",
      " - train_func_1_batch_size=64,dropout_2=0.41106,lr=0.0094198,lstm_1_units=128,lstm_2_units=32,past_seq_len=74,selected_features=['WEEKDAY(datetime)' 'IS_WEEKEND(datetime)' 'MONTH(datetime)'\n",
      " 'IS_AWAKE(datetime)' 'HOUR(datetime)' 'DAY(datetime)']:\tRUNNING\n",
      "\n",
      "== Status ==\n",
      "Using FIFO scheduling algorithm.\n",
      "Resources requested: 4/4 CPUs, 0/0 GPUs (0/1.0 ps, 0/1.0 trainer)\n",
      "Memory usage on this node: 3.0/16.6 GB\n",
      "Result logdir: /home/shane/ray_results/automl\n",
      "Number of trials: 3 ({'RUNNING': 2, 'PENDING': 1})\n",
      "PENDING trials:\n",
      " - train_func_2_batch_size=64,dropout_2=0.25682,lr=0.009007,lstm_1_units=16,lstm_2_units=64,past_seq_len=40,selected_features=['WEEKDAY(datetime)' 'DAY(datetime)' 'IS_AWAKE(datetime)'\n",
      " 'MONTH(datetime)']:\tPENDING\n",
      "RUNNING trials:\n",
      " - train_func_0_batch_size=64,dropout_2=0.39015,lr=0.0055474,lstm_1_units=16,lstm_2_units=16,past_seq_len=73,selected_features=['DAY(datetime)' 'HOUR(datetime)' 'IS_WEEKEND(datetime)'\n",
      " 'IS_BUSY_HOURS(datetime)' 'WEEKDAY(datetime)']:\tRUNNING, [2 CPUs, 0 GPUs], [pid=9119], 23 s, 2 iter\n",
      " - train_func_1_batch_size=64,dropout_2=0.41106,lr=0.0094198,lstm_1_units=128,lstm_2_units=32,past_seq_len=74,selected_features=['WEEKDAY(datetime)' 'IS_WEEKEND(datetime)' 'MONTH(datetime)'\n",
      " 'IS_AWAKE(datetime)' 'HOUR(datetime)' 'DAY(datetime)']:\tRUNNING, [2 CPUs, 0 GPUs], [pid=9184], 15 s, 1 iter\n",
      "\n",
      "== Status ==\n",
      "Using FIFO scheduling algorithm.\n",
      "Resources requested: 4/4 CPUs, 0/0 GPUs (0/1.0 ps, 0/1.0 trainer)\n",
      "Memory usage on this node: 3.0/16.6 GB\n",
      "Result logdir: /home/shane/ray_results/automl\n",
      "Number of trials: 3 ({'RUNNING': 2, 'PENDING': 1})\n",
      "PENDING trials:\n",
      " - train_func_2_batch_size=64,dropout_2=0.25682,lr=0.009007,lstm_1_units=16,lstm_2_units=64,past_seq_len=40,selected_features=['WEEKDAY(datetime)' 'DAY(datetime)' 'IS_AWAKE(datetime)'\n",
      " 'MONTH(datetime)']:\tPENDING\n",
      "RUNNING trials:\n",
      " - train_func_0_batch_size=64,dropout_2=0.39015,lr=0.0055474,lstm_1_units=16,lstm_2_units=16,past_seq_len=73,selected_features=['DAY(datetime)' 'HOUR(datetime)' 'IS_WEEKEND(datetime)'\n",
      " 'IS_BUSY_HOURS(datetime)' 'WEEKDAY(datetime)']:\tRUNNING, [2 CPUs, 0 GPUs], [pid=9119], 33 s, 3 iter\n",
      " - train_func_1_batch_size=64,dropout_2=0.41106,lr=0.0094198,lstm_1_units=128,lstm_2_units=32,past_seq_len=74,selected_features=['WEEKDAY(datetime)' 'IS_WEEKEND(datetime)' 'MONTH(datetime)'\n",
      " 'IS_AWAKE(datetime)' 'HOUR(datetime)' 'DAY(datetime)']:\tRUNNING, [2 CPUs, 0 GPUs], [pid=9184], 27 s, 2 iter\n",
      "\n",
      "== Status ==\n",
      "Using FIFO scheduling algorithm.\n",
      "Resources requested: 4/4 CPUs, 0/0 GPUs (0/1.0 ps, 0/1.0 trainer)\n",
      "Memory usage on this node: 3.0/16.6 GB\n",
      "Result logdir: /home/shane/ray_results/automl\n",
      "Number of trials: 3 ({'RUNNING': 2, 'PENDING': 1})\n",
      "PENDING trials:\n",
      " - train_func_2_batch_size=64,dropout_2=0.25682,lr=0.009007,lstm_1_units=16,lstm_2_units=64,past_seq_len=40,selected_features=['WEEKDAY(datetime)' 'DAY(datetime)' 'IS_AWAKE(datetime)'\n",
      " 'MONTH(datetime)']:\tPENDING\n",
      "RUNNING trials:\n",
      " - train_func_0_batch_size=64,dropout_2=0.39015,lr=0.0055474,lstm_1_units=16,lstm_2_units=16,past_seq_len=73,selected_features=['DAY(datetime)' 'HOUR(datetime)' 'IS_WEEKEND(datetime)'\n",
      " 'IS_BUSY_HOURS(datetime)' 'WEEKDAY(datetime)']:\tRUNNING, [2 CPUs, 0 GPUs], [pid=9119], 33 s, 3 iter\n",
      " - train_func_1_batch_size=64,dropout_2=0.41106,lr=0.0094198,lstm_1_units=128,lstm_2_units=32,past_seq_len=74,selected_features=['WEEKDAY(datetime)' 'IS_WEEKEND(datetime)' 'MONTH(datetime)'\n",
      " 'IS_AWAKE(datetime)' 'HOUR(datetime)' 'DAY(datetime)']:\tRUNNING, [2 CPUs, 0 GPUs], [pid=9184], 40 s, 3 iter\n",
      "\n",
      "== Status ==\n",
      "Using FIFO scheduling algorithm.\n",
      "Resources requested: 4/4 CPUs, 0/0 GPUs (0/1.0 ps, 0/1.0 trainer)\n",
      "Memory usage on this node: 3.0/16.6 GB\n",
      "Result logdir: /home/shane/ray_results/automl\n",
      "Number of trials: 3 ({'RUNNING': 2, 'PENDING': 1})\n",
      "PENDING trials:\n",
      " - train_func_2_batch_size=64,dropout_2=0.25682,lr=0.009007,lstm_1_units=16,lstm_2_units=64,past_seq_len=40,selected_features=['WEEKDAY(datetime)' 'DAY(datetime)' 'IS_AWAKE(datetime)'\n",
      " 'MONTH(datetime)']:\tPENDING\n",
      "RUNNING trials:\n",
      " - train_func_0_batch_size=64,dropout_2=0.39015,lr=0.0055474,lstm_1_units=16,lstm_2_units=16,past_seq_len=73,selected_features=['DAY(datetime)' 'HOUR(datetime)' 'IS_WEEKEND(datetime)'\n",
      " 'IS_BUSY_HOURS(datetime)' 'WEEKDAY(datetime)']:\tRUNNING, [2 CPUs, 0 GPUs], [pid=9119], 43 s, 4 iter\n",
      " - train_func_1_batch_size=64,dropout_2=0.41106,lr=0.0094198,lstm_1_units=128,lstm_2_units=32,past_seq_len=74,selected_features=['WEEKDAY(datetime)' 'IS_WEEKEND(datetime)' 'MONTH(datetime)'\n",
      " 'IS_AWAKE(datetime)' 'HOUR(datetime)' 'DAY(datetime)']:\tRUNNING, [2 CPUs, 0 GPUs], [pid=9184], 52 s, 4 iter\n",
      "\n",
      "== Status ==\n",
      "Using FIFO scheduling algorithm.\n",
      "Resources requested: 4/4 CPUs, 0/0 GPUs (0/1.0 ps, 0/1.0 trainer)\n",
      "Memory usage on this node: 3.0/16.6 GB\n",
      "Result logdir: /home/shane/ray_results/automl\n",
      "Number of trials: 3 ({'RUNNING': 2, 'PENDING': 1})\n",
      "PENDING trials:\n",
      " - train_func_2_batch_size=64,dropout_2=0.25682,lr=0.009007,lstm_1_units=16,lstm_2_units=64,past_seq_len=40,selected_features=['WEEKDAY(datetime)' 'DAY(datetime)' 'IS_AWAKE(datetime)'\n",
      " 'MONTH(datetime)']:\tPENDING\n",
      "RUNNING trials:\n",
      " - train_func_0_batch_size=64,dropout_2=0.39015,lr=0.0055474,lstm_1_units=16,lstm_2_units=16,past_seq_len=73,selected_features=['DAY(datetime)' 'HOUR(datetime)' 'IS_WEEKEND(datetime)'\n",
      " 'IS_BUSY_HOURS(datetime)' 'WEEKDAY(datetime)']:\tRUNNING, [2 CPUs, 0 GPUs], [pid=9119], 64 s, 6 iter\n",
      " - train_func_1_batch_size=64,dropout_2=0.41106,lr=0.0094198,lstm_1_units=128,lstm_2_units=32,past_seq_len=74,selected_features=['WEEKDAY(datetime)' 'IS_WEEKEND(datetime)' 'MONTH(datetime)'\n",
      " 'IS_AWAKE(datetime)' 'HOUR(datetime)' 'DAY(datetime)']:\tRUNNING, [2 CPUs, 0 GPUs], [pid=9184], 52 s, 4 iter\n",
      "\n",
      "== Status ==\n",
      "Using FIFO scheduling algorithm.\n",
      "Resources requested: 4/4 CPUs, 0/0 GPUs (0/1.0 ps, 0/1.0 trainer)\n",
      "Memory usage on this node: 3.0/16.6 GB\n",
      "Result logdir: /home/shane/ray_results/automl\n",
      "Number of trials: 3 ({'RUNNING': 2, 'PENDING': 1})\n",
      "PENDING trials:\n",
      " - train_func_2_batch_size=64,dropout_2=0.25682,lr=0.009007,lstm_1_units=16,lstm_2_units=64,past_seq_len=40,selected_features=['WEEKDAY(datetime)' 'DAY(datetime)' 'IS_AWAKE(datetime)'\n",
      " 'MONTH(datetime)']:\tPENDING\n",
      "RUNNING trials:\n",
      " - train_func_0_batch_size=64,dropout_2=0.39015,lr=0.0055474,lstm_1_units=16,lstm_2_units=16,past_seq_len=73,selected_features=['DAY(datetime)' 'HOUR(datetime)' 'IS_WEEKEND(datetime)'\n",
      " 'IS_BUSY_HOURS(datetime)' 'WEEKDAY(datetime)']:\tRUNNING, [2 CPUs, 0 GPUs], [pid=9119], 74 s, 7 iter\n",
      " - train_func_1_batch_size=64,dropout_2=0.41106,lr=0.0094198,lstm_1_units=128,lstm_2_units=32,past_seq_len=74,selected_features=['WEEKDAY(datetime)' 'IS_WEEKEND(datetime)' 'MONTH(datetime)'\n",
      " 'IS_AWAKE(datetime)' 'HOUR(datetime)' 'DAY(datetime)']:\tRUNNING, [2 CPUs, 0 GPUs], [pid=9184], 64 s, 5 iter\n",
      "\n",
      "== Status ==\n",
      "Using FIFO scheduling algorithm.\n",
      "Resources requested: 4/4 CPUs, 0/0 GPUs (0/1.0 ps, 0/1.0 trainer)\n",
      "Memory usage on this node: 3.0/16.6 GB\n",
      "Result logdir: /home/shane/ray_results/automl\n",
      "Number of trials: 3 ({'RUNNING': 2, 'PENDING': 1})\n",
      "PENDING trials:\n",
      " - train_func_2_batch_size=64,dropout_2=0.25682,lr=0.009007,lstm_1_units=16,lstm_2_units=64,past_seq_len=40,selected_features=['WEEKDAY(datetime)' 'DAY(datetime)' 'IS_AWAKE(datetime)'\n",
      " 'MONTH(datetime)']:\tPENDING\n",
      "RUNNING trials:\n",
      " - train_func_0_batch_size=64,dropout_2=0.39015,lr=0.0055474,lstm_1_units=16,lstm_2_units=16,past_seq_len=73,selected_features=['DAY(datetime)' 'HOUR(datetime)' 'IS_WEEKEND(datetime)'\n",
      " 'IS_BUSY_HOURS(datetime)' 'WEEKDAY(datetime)']:\tRUNNING, [2 CPUs, 0 GPUs], [pid=9119], 84 s, 8 iter\n",
      " - train_func_1_batch_size=64,dropout_2=0.41106,lr=0.0094198,lstm_1_units=128,lstm_2_units=32,past_seq_len=74,selected_features=['WEEKDAY(datetime)' 'IS_WEEKEND(datetime)' 'MONTH(datetime)'\n",
      " 'IS_AWAKE(datetime)' 'HOUR(datetime)' 'DAY(datetime)']:\tRUNNING, [2 CPUs, 0 GPUs], [pid=9184], 76 s, 6 iter\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "== Status ==\n",
      "Using FIFO scheduling algorithm.\n",
      "Resources requested: 4/4 CPUs, 0/0 GPUs (0/1.0 ps, 0/1.0 trainer)\n",
      "Memory usage on this node: 3.0/16.6 GB\n",
      "Result logdir: /home/shane/ray_results/automl\n",
      "Number of trials: 3 ({'RUNNING': 2, 'PENDING': 1})\n",
      "PENDING trials:\n",
      " - train_func_2_batch_size=64,dropout_2=0.25682,lr=0.009007,lstm_1_units=16,lstm_2_units=64,past_seq_len=40,selected_features=['WEEKDAY(datetime)' 'DAY(datetime)' 'IS_AWAKE(datetime)'\n",
      " 'MONTH(datetime)']:\tPENDING\n",
      "RUNNING trials:\n",
      " - train_func_0_batch_size=64,dropout_2=0.39015,lr=0.0055474,lstm_1_units=16,lstm_2_units=16,past_seq_len=73,selected_features=['DAY(datetime)' 'HOUR(datetime)' 'IS_WEEKEND(datetime)'\n",
      " 'IS_BUSY_HOURS(datetime)' 'WEEKDAY(datetime)']:\tRUNNING, [2 CPUs, 0 GPUs], [pid=9119], 94 s, 9 iter\n",
      " - train_func_1_batch_size=64,dropout_2=0.41106,lr=0.0094198,lstm_1_units=128,lstm_2_units=32,past_seq_len=74,selected_features=['WEEKDAY(datetime)' 'IS_WEEKEND(datetime)' 'MONTH(datetime)'\n",
      " 'IS_AWAKE(datetime)' 'HOUR(datetime)' 'DAY(datetime)']:\tRUNNING, [2 CPUs, 0 GPUs], [pid=9184], 89 s, 7 iter\n",
      "\n",
      "== Status ==\n",
      "Using FIFO scheduling algorithm.\n",
      "Resources requested: 4/4 CPUs, 0/0 GPUs (0/1.0 ps, 0/1.0 trainer)\n",
      "Memory usage on this node: 3.0/16.6 GB\n",
      "Result logdir: /home/shane/ray_results/automl\n",
      "Number of trials: 3 ({'RUNNING': 2, 'PENDING': 1})\n",
      "PENDING trials:\n",
      " - train_func_2_batch_size=64,dropout_2=0.25682,lr=0.009007,lstm_1_units=16,lstm_2_units=64,past_seq_len=40,selected_features=['WEEKDAY(datetime)' 'DAY(datetime)' 'IS_AWAKE(datetime)'\n",
      " 'MONTH(datetime)']:\tPENDING\n",
      "RUNNING trials:\n",
      " - train_func_0_batch_size=64,dropout_2=0.39015,lr=0.0055474,lstm_1_units=16,lstm_2_units=16,past_seq_len=73,selected_features=['DAY(datetime)' 'HOUR(datetime)' 'IS_WEEKEND(datetime)'\n",
      " 'IS_BUSY_HOURS(datetime)' 'WEEKDAY(datetime)']:\tRUNNING, [2 CPUs, 0 GPUs], [pid=9119], 94 s, 9 iter\n",
      " - train_func_1_batch_size=64,dropout_2=0.41106,lr=0.0094198,lstm_1_units=128,lstm_2_units=32,past_seq_len=74,selected_features=['WEEKDAY(datetime)' 'IS_WEEKEND(datetime)' 'MONTH(datetime)'\n",
      " 'IS_AWAKE(datetime)' 'HOUR(datetime)' 'DAY(datetime)']:\tRUNNING, [2 CPUs, 0 GPUs], [pid=9184], 101 s, 8 iter\n",
      "\n",
      "\u001b[2m\u001b[36m(pid=9118)\u001b[0m LSTM is selected.\n",
      "\u001b[2m\u001b[36m(pid=9118)\u001b[0m WARNING:tensorflow:From /home/shane/.local/lib/python3.6/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "\u001b[2m\u001b[36m(pid=9118)\u001b[0m Instructions for updating:\n",
      "\u001b[2m\u001b[36m(pid=9118)\u001b[0m If using Keras pass *_constraint arguments to layers.\n",
      "\u001b[2m\u001b[36m(pid=9118)\u001b[0m LSTM is selected.\n",
      "\u001b[2m\u001b[36m(pid=9118)\u001b[0m WARNING:tensorflow:From /home/shane/.local/lib/python3.6/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "\u001b[2m\u001b[36m(pid=9118)\u001b[0m Instructions for updating:\n",
      "\u001b[2m\u001b[36m(pid=9118)\u001b[0m If using Keras pass *_constraint arguments to layers.\n",
      "\u001b[2m\u001b[36m(pid=9118)\u001b[0m WARNING:tensorflow:From /home/shane/.local/lib/python3.6/site-packages/tensorflow_core/python/ops/math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "\u001b[2m\u001b[36m(pid=9118)\u001b[0m Instructions for updating:\n",
      "\u001b[2m\u001b[36m(pid=9118)\u001b[0m Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "\u001b[2m\u001b[36m(pid=9118)\u001b[0m WARNING:tensorflow:From /home/shane/.local/lib/python3.6/site-packages/tensorflow_core/python/ops/math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "\u001b[2m\u001b[36m(pid=9118)\u001b[0m Instructions for updating:\n",
      "\u001b[2m\u001b[36m(pid=9118)\u001b[0m Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "\u001b[2m\u001b[36m(pid=9118)\u001b[0m 2020-04-05 17:47:22.771295: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/shane/torch/install/lib:/home/shane/torch/install/lib:/home/shane/torch/install/lib:\n",
      "\u001b[2m\u001b[36m(pid=9118)\u001b[0m 2020-04-05 17:47:22.771348: E tensorflow/stream_executor/cuda/cuda_driver.cc:318] failed call to cuInit: UNKNOWN ERROR (303)\n",
      "\u001b[2m\u001b[36m(pid=9118)\u001b[0m 2020-04-05 17:47:22.771372: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (shane-workstation): /proc/driver/nvidia/version does not exist\n",
      "\u001b[2m\u001b[36m(pid=9118)\u001b[0m 2020-04-05 17:47:22.771796: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA\n",
      "\u001b[2m\u001b[36m(pid=9118)\u001b[0m 2020-04-05 17:47:22.799066: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 3591790000 Hz\n",
      "\u001b[2m\u001b[36m(pid=9118)\u001b[0m 2020-04-05 17:47:22.799519: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f88bd0f39a0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n",
      "\u001b[2m\u001b[36m(pid=9118)\u001b[0m 2020-04-05 17:47:22.799543: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version\n",
      "\u001b[2m\u001b[36m(pid=9118)\u001b[0m 2020-04-05 17:47:22.771295: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/shane/torch/install/lib:/home/shane/torch/install/lib:/home/shane/torch/install/lib:\n",
      "\u001b[2m\u001b[36m(pid=9118)\u001b[0m 2020-04-05 17:47:22.771348: E tensorflow/stream_executor/cuda/cuda_driver.cc:318] failed call to cuInit: UNKNOWN ERROR (303)\n",
      "\u001b[2m\u001b[36m(pid=9118)\u001b[0m 2020-04-05 17:47:22.771372: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (shane-workstation): /proc/driver/nvidia/version does not exist\n",
      "\u001b[2m\u001b[36m(pid=9118)\u001b[0m 2020-04-05 17:47:22.771796: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA\n",
      "\u001b[2m\u001b[36m(pid=9118)\u001b[0m 2020-04-05 17:47:22.799066: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 3591790000 Hz\n",
      "\u001b[2m\u001b[36m(pid=9118)\u001b[0m 2020-04-05 17:47:22.799519: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f88bd0f39a0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n",
      "\u001b[2m\u001b[36m(pid=9118)\u001b[0m 2020-04-05 17:47:22.799543: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version\n",
      "== Status ==\n",
      "Using FIFO scheduling algorithm.\n",
      "Resources requested: 4/4 CPUs, 0/0 GPUs (0/1.0 ps, 0/1.0 trainer)\n",
      "Memory usage on this node: 3.0/16.6 GB\n",
      "Result logdir: /home/shane/ray_results/automl\n",
      "Number of trials: 3 ({'TERMINATED': 1, 'RUNNING': 2})\n",
      "RUNNING trials:\n",
      " - train_func_1_batch_size=64,dropout_2=0.41106,lr=0.0094198,lstm_1_units=128,lstm_2_units=32,past_seq_len=74,selected_features=['WEEKDAY(datetime)' 'IS_WEEKEND(datetime)' 'MONTH(datetime)'\n",
      " 'IS_AWAKE(datetime)' 'HOUR(datetime)' 'DAY(datetime)']:\tRUNNING, [2 CPUs, 0 GPUs], [pid=9184], 112 s, 9 iter\n",
      " - train_func_2_batch_size=64,dropout_2=0.25682,lr=0.009007,lstm_1_units=16,lstm_2_units=64,past_seq_len=40,selected_features=['WEEKDAY(datetime)' 'DAY(datetime)' 'IS_AWAKE(datetime)'\n",
      " 'MONTH(datetime)']:\tRUNNING\n",
      "TERMINATED trials:\n",
      " - train_func_0_batch_size=64,dropout_2=0.39015,lr=0.0055474,lstm_1_units=16,lstm_2_units=16,past_seq_len=73,selected_features=['DAY(datetime)' 'HOUR(datetime)' 'IS_WEEKEND(datetime)'\n",
      " 'IS_BUSY_HOURS(datetime)' 'WEEKDAY(datetime)']:\tTERMINATED, [2 CPUs, 0 GPUs], [pid=9119], 104 s, 10 iter\n",
      "\n",
      "== Status ==\n",
      "Using FIFO scheduling algorithm.\n",
      "Resources requested: 4/4 CPUs, 0/0 GPUs (0/1.0 ps, 0/1.0 trainer)\n",
      "Memory usage on this node: 3.0/16.6 GB\n",
      "Result logdir: /home/shane/ray_results/automl\n",
      "Number of trials: 3 ({'TERMINATED': 1, 'RUNNING': 2})\n",
      "RUNNING trials:\n",
      " - train_func_1_batch_size=64,dropout_2=0.41106,lr=0.0094198,lstm_1_units=128,lstm_2_units=32,past_seq_len=74,selected_features=['WEEKDAY(datetime)' 'IS_WEEKEND(datetime)' 'MONTH(datetime)'\n",
      " 'IS_AWAKE(datetime)' 'HOUR(datetime)' 'DAY(datetime)']:\tRUNNING, [2 CPUs, 0 GPUs], [pid=9184], 112 s, 9 iter\n",
      " - train_func_2_batch_size=64,dropout_2=0.25682,lr=0.009007,lstm_1_units=16,lstm_2_units=64,past_seq_len=40,selected_features=['WEEKDAY(datetime)' 'DAY(datetime)' 'IS_AWAKE(datetime)'\n",
      " 'MONTH(datetime)']:\tRUNNING, [2 CPUs, 0 GPUs], [pid=9118], 12 s, 1 iter\n",
      "TERMINATED trials:\n",
      " - train_func_0_batch_size=64,dropout_2=0.39015,lr=0.0055474,lstm_1_units=16,lstm_2_units=16,past_seq_len=73,selected_features=['DAY(datetime)' 'HOUR(datetime)' 'IS_WEEKEND(datetime)'\n",
      " 'IS_BUSY_HOURS(datetime)' 'WEEKDAY(datetime)']:\tTERMINATED, [2 CPUs, 0 GPUs], [pid=9119], 104 s, 10 iter\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "== Status ==\n",
      "Using FIFO scheduling algorithm.\n",
      "Resources requested: 4/4 CPUs, 0/0 GPUs (0/1.0 ps, 0/1.0 trainer)\n",
      "Memory usage on this node: 3.0/16.6 GB\n",
      "Result logdir: /home/shane/ray_results/automl\n",
      "Number of trials: 3 ({'TERMINATED': 1, 'RUNNING': 2})\n",
      "RUNNING trials:\n",
      " - train_func_1_batch_size=64,dropout_2=0.41106,lr=0.0094198,lstm_1_units=128,lstm_2_units=32,past_seq_len=74,selected_features=['WEEKDAY(datetime)' 'IS_WEEKEND(datetime)' 'MONTH(datetime)'\n",
      " 'IS_AWAKE(datetime)' 'HOUR(datetime)' 'DAY(datetime)']:\tRUNNING, [2 CPUs, 0 GPUs], [pid=9184], 112 s, 9 iter\n",
      " - train_func_2_batch_size=64,dropout_2=0.25682,lr=0.009007,lstm_1_units=16,lstm_2_units=64,past_seq_len=40,selected_features=['WEEKDAY(datetime)' 'DAY(datetime)' 'IS_AWAKE(datetime)'\n",
      " 'MONTH(datetime)']:\tRUNNING, [2 CPUs, 0 GPUs], [pid=9118], 18 s, 2 iter\n",
      "TERMINATED trials:\n",
      " - train_func_0_batch_size=64,dropout_2=0.39015,lr=0.0055474,lstm_1_units=16,lstm_2_units=16,past_seq_len=73,selected_features=['DAY(datetime)' 'HOUR(datetime)' 'IS_WEEKEND(datetime)'\n",
      " 'IS_BUSY_HOURS(datetime)' 'WEEKDAY(datetime)']:\tTERMINATED, [2 CPUs, 0 GPUs], [pid=9119], 104 s, 10 iter\n",
      "\n",
      "== Status ==\n",
      "Using FIFO scheduling algorithm.\n",
      "Resources requested: 2/4 CPUs, 0/0 GPUs (0/1.0 ps, 0/1.0 trainer)\n",
      "Memory usage on this node: 3.0/16.6 GB\n",
      "Result logdir: /home/shane/ray_results/automl\n",
      "Number of trials: 3 ({'TERMINATED': 2, 'RUNNING': 1})\n",
      "RUNNING trials:\n",
      " - train_func_2_batch_size=64,dropout_2=0.25682,lr=0.009007,lstm_1_units=16,lstm_2_units=64,past_seq_len=40,selected_features=['WEEKDAY(datetime)' 'DAY(datetime)' 'IS_AWAKE(datetime)'\n",
      " 'MONTH(datetime)']:\tRUNNING, [2 CPUs, 0 GPUs], [pid=9118], 24 s, 4 iter\n",
      "TERMINATED trials:\n",
      " - train_func_0_batch_size=64,dropout_2=0.39015,lr=0.0055474,lstm_1_units=16,lstm_2_units=16,past_seq_len=73,selected_features=['DAY(datetime)' 'HOUR(datetime)' 'IS_WEEKEND(datetime)'\n",
      " 'IS_BUSY_HOURS(datetime)' 'WEEKDAY(datetime)']:\tTERMINATED, [2 CPUs, 0 GPUs], [pid=9119], 104 s, 10 iter\n",
      " - train_func_1_batch_size=64,dropout_2=0.41106,lr=0.0094198,lstm_1_units=128,lstm_2_units=32,past_seq_len=74,selected_features=['WEEKDAY(datetime)' 'IS_WEEKEND(datetime)' 'MONTH(datetime)'\n",
      " 'IS_AWAKE(datetime)' 'HOUR(datetime)' 'DAY(datetime)']:\tTERMINATED, [2 CPUs, 0 GPUs], [pid=9184], 124 s, 10 iter\n",
      "\n",
      "== Status ==\n",
      "Using FIFO scheduling algorithm.\n",
      "Resources requested: 2/4 CPUs, 0/0 GPUs (0/1.0 ps, 0/1.0 trainer)\n",
      "Memory usage on this node: 3.0/16.6 GB\n",
      "Result logdir: /home/shane/ray_results/automl\n",
      "Number of trials: 3 ({'TERMINATED': 2, 'RUNNING': 1})\n",
      "RUNNING trials:\n",
      " - train_func_2_batch_size=64,dropout_2=0.25682,lr=0.009007,lstm_1_units=16,lstm_2_units=64,past_seq_len=40,selected_features=['WEEKDAY(datetime)' 'DAY(datetime)' 'IS_AWAKE(datetime)'\n",
      " 'MONTH(datetime)']:\tRUNNING, [2 CPUs, 0 GPUs], [pid=9118], 30 s, 6 iter\n",
      "TERMINATED trials:\n",
      " - train_func_0_batch_size=64,dropout_2=0.39015,lr=0.0055474,lstm_1_units=16,lstm_2_units=16,past_seq_len=73,selected_features=['DAY(datetime)' 'HOUR(datetime)' 'IS_WEEKEND(datetime)'\n",
      " 'IS_BUSY_HOURS(datetime)' 'WEEKDAY(datetime)']:\tTERMINATED, [2 CPUs, 0 GPUs], [pid=9119], 104 s, 10 iter\n",
      " - train_func_1_batch_size=64,dropout_2=0.41106,lr=0.0094198,lstm_1_units=128,lstm_2_units=32,past_seq_len=74,selected_features=['WEEKDAY(datetime)' 'IS_WEEKEND(datetime)' 'MONTH(datetime)'\n",
      " 'IS_AWAKE(datetime)' 'HOUR(datetime)' 'DAY(datetime)']:\tTERMINATED, [2 CPUs, 0 GPUs], [pid=9184], 124 s, 10 iter\n",
      "\n",
      "== Status ==\n",
      "Using FIFO scheduling algorithm.\n",
      "Resources requested: 2/4 CPUs, 0/0 GPUs (0/1.0 ps, 0/1.0 trainer)\n",
      "Memory usage on this node: 3.0/16.6 GB\n",
      "Result logdir: /home/shane/ray_results/automl\n",
      "Number of trials: 3 ({'TERMINATED': 2, 'RUNNING': 1})\n",
      "RUNNING trials:\n",
      " - train_func_2_batch_size=64,dropout_2=0.25682,lr=0.009007,lstm_1_units=16,lstm_2_units=64,past_seq_len=40,selected_features=['WEEKDAY(datetime)' 'DAY(datetime)' 'IS_AWAKE(datetime)'\n",
      " 'MONTH(datetime)']:\tRUNNING, [2 CPUs, 0 GPUs], [pid=9118], 36 s, 8 iter\n",
      "TERMINATED trials:\n",
      " - train_func_0_batch_size=64,dropout_2=0.39015,lr=0.0055474,lstm_1_units=16,lstm_2_units=16,past_seq_len=73,selected_features=['DAY(datetime)' 'HOUR(datetime)' 'IS_WEEKEND(datetime)'\n",
      " 'IS_BUSY_HOURS(datetime)' 'WEEKDAY(datetime)']:\tTERMINATED, [2 CPUs, 0 GPUs], [pid=9119], 104 s, 10 iter\n",
      " - train_func_1_batch_size=64,dropout_2=0.41106,lr=0.0094198,lstm_1_units=128,lstm_2_units=32,past_seq_len=74,selected_features=['WEEKDAY(datetime)' 'IS_WEEKEND(datetime)' 'MONTH(datetime)'\n",
      " 'IS_AWAKE(datetime)' 'HOUR(datetime)' 'DAY(datetime)']:\tTERMINATED, [2 CPUs, 0 GPUs], [pid=9184], 124 s, 10 iter\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-04-05 17:48:02,052\tINFO ray_trial_executor.py:180 -- Destroying actor for trial train_func_2_batch_size=64,dropout_2=0.25682,lr=0.009007,lstm_1_units=16,lstm_2_units=64,past_seq_len=40,selected_features=['WEEKDAY(datetime)' 'DAY(datetime)' 'IS_AWAKE(datetime)'\n",
      " 'MONTH(datetime)']. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "== Status ==\n",
      "Using FIFO scheduling algorithm.\n",
      "Resources requested: 0/4 CPUs, 0/0 GPUs (0/1.0 ps, 0/1.0 trainer)\n",
      "Memory usage on this node: 3.0/16.6 GB\n",
      "Result logdir: /home/shane/ray_results/automl\n",
      "Number of trials: 3 ({'TERMINATED': 3})\n",
      "TERMINATED trials:\n",
      " - train_func_0_batch_size=64,dropout_2=0.39015,lr=0.0055474,lstm_1_units=16,lstm_2_units=16,past_seq_len=73,selected_features=['DAY(datetime)' 'HOUR(datetime)' 'IS_WEEKEND(datetime)'\n",
      " 'IS_BUSY_HOURS(datetime)' 'WEEKDAY(datetime)']:\tTERMINATED, [2 CPUs, 0 GPUs], [pid=9119], 104 s, 10 iter\n",
      " - train_func_1_batch_size=64,dropout_2=0.41106,lr=0.0094198,lstm_1_units=128,lstm_2_units=32,past_seq_len=74,selected_features=['WEEKDAY(datetime)' 'IS_WEEKEND(datetime)' 'MONTH(datetime)'\n",
      " 'IS_AWAKE(datetime)' 'HOUR(datetime)' 'DAY(datetime)']:\tTERMINATED, [2 CPUs, 0 GPUs], [pid=9184], 124 s, 10 iter\n",
      " - train_func_2_batch_size=64,dropout_2=0.25682,lr=0.009007,lstm_1_units=16,lstm_2_units=64,past_seq_len=40,selected_features=['WEEKDAY(datetime)' 'DAY(datetime)' 'IS_AWAKE(datetime)'\n",
      " 'MONTH(datetime)']:\tTERMINATED, [2 CPUs, 0 GPUs], [pid=9118], 42 s, 10 iter\n",
      "\n",
      "== Status ==\n",
      "Using FIFO scheduling algorithm.\n",
      "Resources requested: 0/4 CPUs, 0/0 GPUs (0/1.0 ps, 0/1.0 trainer)\n",
      "Memory usage on this node: 3.0/16.6 GB\n",
      "Result logdir: /home/shane/ray_results/automl\n",
      "Number of trials: 3 ({'TERMINATED': 3})\n",
      "TERMINATED trials:\n",
      " - train_func_0_batch_size=64,dropout_2=0.39015,lr=0.0055474,lstm_1_units=16,lstm_2_units=16,past_seq_len=73,selected_features=['DAY(datetime)' 'HOUR(datetime)' 'IS_WEEKEND(datetime)'\n",
      " 'IS_BUSY_HOURS(datetime)' 'WEEKDAY(datetime)']:\tTERMINATED, [2 CPUs, 0 GPUs], [pid=9119], 104 s, 10 iter\n",
      " - train_func_1_batch_size=64,dropout_2=0.41106,lr=0.0094198,lstm_1_units=128,lstm_2_units=32,past_seq_len=74,selected_features=['WEEKDAY(datetime)' 'IS_WEEKEND(datetime)' 'MONTH(datetime)'\n",
      " 'IS_AWAKE(datetime)' 'HOUR(datetime)' 'DAY(datetime)']:\tTERMINATED, [2 CPUs, 0 GPUs], [pid=9184], 124 s, 10 iter\n",
      " - train_func_2_batch_size=64,dropout_2=0.25682,lr=0.009007,lstm_1_units=16,lstm_2_units=64,past_seq_len=40,selected_features=['WEEKDAY(datetime)' 'DAY(datetime)' 'IS_AWAKE(datetime)'\n",
      " 'MONTH(datetime)']:\tTERMINATED, [2 CPUs, 0 GPUs], [pid=9118], 42 s, 10 iter\n",
      "\n",
      "The best configurations are:\n",
      "selected_features : ['DAY(datetime)' 'HOUR(datetime)' 'IS_WEEKEND(datetime)'\n",
      " 'IS_BUSY_HOURS(datetime)' 'WEEKDAY(datetime)']\n",
      "model : LSTM\n",
      "lstm_1_units : 16\n",
      "dropout_1 : 0.2\n",
      "lstm_2_units : 16\n",
      "dropout_2 : 0.39014785774361804\n",
      "lr : 0.005547442119849576\n",
      "batch_size : 64\n",
      "epochs : 1\n",
      "past_seq_len : 73\n",
      "WARNING:tensorflow:From /home/shane/.local/lib/python3.6/site-packages/tensorflow_core/python/ops/init_ops.py:97: calling GlorotUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
      "WARNING:tensorflow:From /home/shane/.local/lib/python3.6/site-packages/tensorflow_core/python/ops/init_ops.py:97: calling Orthogonal.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
      "WARNING:tensorflow:From /home/shane/.local/lib/python3.6/site-packages/tensorflow_core/python/ops/init_ops.py:97: calling Zeros.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
      "WARNING:tensorflow:From /home/shane/.local/lib/python3.6/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n",
      "WARNING:tensorflow:OMP_NUM_THREADS is no longer used by the default Keras config. To configure the number of threads, use tf.config.threading APIs.\n",
      "WARNING:tensorflow:From /home/shane/.local/lib/python3.6/site-packages/tensorflow_core/python/ops/math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "CPU times: user 5.36 s, sys: 775 ms, total: 6.13 s\n",
      "Wall time: 2min 32s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "ts_pipeline = trainer.fit(train_df, val_df, \n",
    "                          recipe=LSTMGridRandomRecipe(\n",
    "                              num_rand_samples=1,\n",
    "                              epochs=1,\n",
    "                              look_back=look_back, \n",
    "                              batch_size=[64]),\n",
    "                          metric=\"mse\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get a `TSPipeline` after training. Let's print the hyper paramters selected.\n",
    "Note that `past_seq_len` is the lookback value that is automatically chosen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:48:10.610297Z",
     "start_time": "2020-04-05T09:48:10.607153Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mean': [470.45753424657534,\n",
       "  15.558219178082192,\n",
       "  11.0,\n",
       "  0.2842465753424658,\n",
       "  0.25,\n",
       "  2.98972602739726],\n",
       " 'scale': [237.54721125499952,\n",
       "  8.827884346441833,\n",
       "  6.904105059069326,\n",
       "  0.45105483009113834,\n",
       "  0.4330127018922193,\n",
       "  2.000829603694729],\n",
       " 'future_seq_len': 1,\n",
       " 'dt_col': 'datetime',\n",
       " 'target_col': 'AvgRate',\n",
       " 'extra_features_col': None,\n",
       " 'drop_missing': True,\n",
       " 'model': 'LSTM',\n",
       " 'metric': 'mean_squared_error',\n",
       " 'batch_size': 64,\n",
       " 'selected_features': ['DAY(datetime)',\n",
       "  'HOUR(datetime)',\n",
       "  'IS_WEEKEND(datetime)',\n",
       "  'IS_BUSY_HOURS(datetime)',\n",
       "  'WEEKDAY(datetime)'],\n",
       " 'lstm_1_units': 16,\n",
       " 'dropout_1': 0.2,\n",
       " 'lstm_2_units': 16,\n",
       " 'dropout_2': 0.39014785774361804,\n",
       " 'lr': 0.005547442119849576,\n",
       " 'epochs': 1,\n",
       " 'past_seq_len': 73}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_pipeline.internal.config"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use it to do prediction, evaluation or incremental fitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:48:18.212911Z",
     "start_time": "2020-04-05T09:48:17.672724Z"
    }
   },
   "outputs": [],
   "source": [
    "pred_df = ts_pipeline.predict(test_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "plot actual and prediction values for `AvgRate` KPI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:48:20.239860Z",
     "start_time": "2020-04-05T09:48:20.074644Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Ijs/EG97whqRq56pVqzB37lwAcoBx9tlnJ236wQ9+AABYvnw5DjvsMMyfPx/77rsvbr/99o26Dg4ODg4OaSxaBDzL1rXrP/zud8Bdd3W6FQ7TBdMm60gKZ54J3H9/a7c5fz7wrW9lrvL4449j0aJFOPTQQ/HBD34Q3/ve93DWWWcBALbYYgvcd999WLVqFU488UTceuutmDVrFi688EJcdNFF+MxnPoPTTz8dv//97/HKV74SCxcutO7jjDPOwOtf/3pcf/31iKIIIyMjuOCCC/Dwww/jfnXMt9xyC5544gn85S9/gRACxx9/PBYvXoxZs2bh6quvxv33348wDLFgwQK86lWvSm1/s802w/z58/HHP/4Rhx9+OG688UYcddRRKJfLOPHEE3H66acDAL74xS9i0aJF+OQnP5nr9C1atAibbbYZ/vrXv6JareLQQw/FkUceiZ///Oc46qij8IUvfAFRFFnJu4ODg4PD1PDYY8BppwFHHw38+tedbk178KY3yU8hOtsOh+mB6Um0O4SddtoJhx56KADgve99Ly6++OKEaGvifNddd+GRRx5J1qvVajjkkEPw2GOPYdddd8Vuu+2W/P6yyy6bsI/f//73+J//+R8AgO/72GyzzbB27drUOrfccgtuueUWHHDAAQCAkZERPPHEExgeHsbb3/52zJw5E4C0pNiwcOFCXHPNNTj88MNx9dVX41/+5V8AAA8//DC++MUvYt26dRgZGcFRRx2V+9zccsstePDBB3HdddcBkMr5E088gYMOOggf/OAHEQQBTjjhBMyfPz/3Nh0cHBwcsnHzzfJzm2062w4Hh37F9CTaTZTnotCYIcP8/6xZswDI3NBvfvOb8ZOf/CS17v0tVOCFEPjc5z6Hj3zkI6nvv5XzvBx//PH4/Oc/jzVr1uDee+/FG9/4RgDAqaeeil/84heYN28errzyStx2220TflsqlRLbiZmWTwiB73znO1ZyvnjxYtx000049dRT8elPfxrve9/78h6qg4ODg0MGfvfVewAciJ137nRLHBz6E86j3UY899xzuPPOOwEAV111FV772tdOWOfggw/GHXfcgSeffBKA9ED/4x//wJ577olnnnkGTz31FABMIOIaRxxxBC699FIA0ve8fv16zJ49G8PDw8k6Rx11FK644orE+/3888/jpZdewmGHHYZf/OIXGB8fx/DwMH71q19Z97HJJpvgoIMOwqc+9Skce+yx8H0fADA8PIztttsOQRDgxz/+sfW3c+fOxb333gsAiXqt23TppZciCAIAwD/+8Q+Mjo7i2WefxTbbbIPTTz8dp512Gu677z7rdh0cHBwcJo8Xl0v/RBh2uCEODn0KR7TbiD322AOXXHIJ9tprL6xduxYf+9jHJqyz1VZb4corr8S73/1u7L///oltZGhoCJdddhne+ta3YsGCBdh6662t+/j2t7+NP/zhD9hvv/3wqle9Co888gi22GILHHroodh3331x9tln48gjj8R73vMeHHLIIdhvv/1w0kknYXh4GAsWLMDChQsxb948HHPMMTjooIPYY1m4cCH+93//N+UV/4/PfhavOeAAHPpP/4Q999zT+ruzzjoL3/3upZg//wCsWrUq+f60007D3nvvjQULFmDffffFRz7yEYRhiNtuuw3z5s3DAQccgGuuuQaf+tSn8p7utuC554DPfQ74/e873RIHBweHyWM9NgMATCHhlYODQw6Q6MNogAMPPFDozBYajz76KPbaa68OtUhmHTn22GPx8MMPd6wNheORR4CxMWDvvQHl825EHAP33QcMDgL77dea3Xby2n75y8AXvgC87nXA4sUdaYKDg4PDlLENrcBL2Ab/9q8Rvn6R3+nmtAXatdmH9MehQyCie4UQB9qWOUXboXXQvZfyYNugpyf7RT3RSVBch+3g4NCL0Ip2uGG8wy1xcOhPOKLdJsydO7e/1WwAq6KX4R4ciCjkWaeyYKNcblOjCsa4ejc5ou3g4NBrqFaBKoYAANGIS53q4FAEHNF2aBlerG0OAKjV+HU00S71Sb4bnTglQ8R3cHBw6Eps2FD/Oxp2iraDQxFwRNuhhZDWEZHBOvtV0XZE28HBodewfn3972jUEe1exdNPAzfd1OlWOHDoE13RoSugLdpRc+tIvynazjri4ODQazCJdjha7VxDHDYKBxwgZyfce6g74RRth5Yjj0e7X+AUbQcHh15FStHO6LcduhvaAjTuJiW6Eo5odyluu+02/PnPf96obWyyySYtactkt5NH0e4XOI+2g4NDryJFtPskE9Rk0G8K8Jo1nW6Bgw2OaHcpWkG02w2d3S+rw+43Quqyjjg4OPQqtBLqI0QYEr/i7bcDqpJwP6HfhB9HtLsTjmi3ESeccAJe9apXYZ999sFll12WfH/zzTdjwYIFmDdvHo444gg888wz+P73v49vfvObmD9/Pm6//XaceuqpqZLlWmUeGRnBEUccgQULFmC//fbDL3/5y8w2nHPOObjkkkuS/5933nn4+te/nms7N9xwG9761mOT/3/iE5/AlVdeCQC49957ceqH3oR//udX4eQPnITly5cDAC6++GLsvffe2H///fGud70rIaT9QkynpXXkG98AZs/udCscHBw2ElrR3hxreIFkfBw47DBZiKzPUO0TW/rMkjyQ1as73BAHK/okJG1yOPNM4P77W7vN+fOBb30re50rrrgCm2++OcbHx3HQQQfhHe94B+I4xumnn47Fixdj1113xZo1a7D55pvjox/9KDbZZBOcddZZAIBFixZZtzk0NITrr78em266KVatWoWDDz4Yxx9/PIjs6sTChQtx5pln4uMf/zgA4Kc//Sl+85vfNN3O2BiwfLndAxYEAT75yU/ioq9dixmb7oB7/rgIX/jCF3DFFVfgggsuwJIlSzA4OIh169bhxRdzntAewbS0jqh7EnEMeG6s7uDQqxgdEQAIm2IDIq4P053c0qXA888DO+zQruYVjtr6cWD2jE43Y6MxK1yPMWyNNc+PA+j94+k3TEui3SlcfPHFuP766wEAS5cuxRNPPIGVK1fisMMOw6677goA2HzzzSe1TSEEPv/5z2Px4sXwPA/PP/88VqxYgW233da6/gEHHICXXnoJL7zwAlauXIk5c+Zgp512QhAEmdvRaoeNUD7++ON4+OGHcfrH3oJYEDxRw8677ggA2H///XHKKafghBNOwAknnAARhABKfadoi1Ae17TC+Dgwa1anW+Hg4DBFxGEEoIRBVBFFjHXE9FesWNFfRPvT5wA//Xanm7HRmOFVgRhY8+QaAP1zffoFhTEDIroCwLEAXhJC7Ku+2xzANQDmAngGwDuFEGtJyqbfBvAWAGMAThVC3Kd+834AX1Sb/U8hxA83tm3NlOcicNttt+HWW2/FnXfeiZkzZ+INb3gDKlopyIFSqYRYsdw4jlFTVWF+/OMfY+XKlbj33ntRLpcxd+7cpts9+eSTcd111+HFF1/EwoULc22HCPD9EuK4Pr+olwshsM8+++D7l9yOIC5h6/Ja7DxvDgDgpptuwuLFi/GrX/0K559/Pq7577uB0qa5j7vbkSjajz4OYJ+OtqVt8H058hobc0TbwaGHEQfynVJGgDDy7SsFARbjdXg9FmPZC3/DDgva2MACYIpF1Uef7lxDWoiZ5QCoAquXbIAj2t2HIud9rwRwdMN35wD4nRBiNwC/U/8HgGMA7Kb+fRjApUBCzM8F8BoArwZwLhHNKbDNhWH9+vWYM2cOZs6cicceewx33XUXAODggw/G4sWLsWTJEgDAGhXNMHv2bAwPDye/nzt3Lu69914AwA033IBAqQzr16/H1ltvjXK5jD/84Q949tlnm7Zl4cKFuPrqq3Hdddfh5JNPzr2dbbfdBU89+Qiqo6NYt24dfve73wEA9thjD6xcuRJ/e0AeU6UW4u9//zviOMbSpUtx+OGH48ILL8T69esxMj4qN9Ynknbi0Z5O4Q6Dg/JzdLSz7XBwcNgoRDUpnJQR8NaRIMBX8DkAwN8eHWpTy4pDGNb/rs3YrHMNaSG8stRM1zw/1uGWONhQmKIthFhMRHMbvn4bgDeov38I4DYAn1Xf/48QQgC4i4heRkTbqXV/K4RYAwBE9FtI8v6TotpdFI4++mh8//vfx1577YU99tgDBx98MABgq622wmWXXYYTTzwRcRxj6623xm9/+1scd9xxOOmkk/DLX/4S3/nOd3D66afjbW97G+bNm4ejjz4as5SSeMopp+C4447DfvvthwMPPBB77rln07bss88+GB4exg477IDtttsu13aIgG233QlHvelE7DtvHnZ9+ctxwAEHAAAGBgZw7bXX4UMfOgMjI+shoho+c86nsfvuu+O9730v1q9fDyEEzjjjDMyePQdVACKKATAKSg+hUpEeR4GMiP1+w+CgVLPHXKfu4NDLiMMYhBglhIhi3jqyDNIKuNXs/LOw3Qoz6LNfiHaVpPix+sWwyZoOnUC7TaXbCCGWq79fBLCN+nsHAEuN9Zap77jvJ4CIPgyphmPnnXduYZNbg8HBQfz617+2LjvmmGNwzDHHpL7bfffd8eCDD6a+0yo4AFx44YUAgC233BJ33nmndbsjGemYHnroodT/m21Hi5dnnXE+rrj8grqqqTBv3nxcdtliAMCm3jB2XyCzUvzpT39KrffgPSrMO4rQD0R7WiraAwPy0ynaDg49jTiM4CmiHUZMH1arYSl2AgCIsPeTbaeI9mB/ZE8aD2WfvGF9f8wU9xs6xg6Uet2yu0IIcZkQ4kAhxIFbbbVVqzZbKJ57Dnj00U63Ih90EhPugpmdVxbp1Mqv6IM0HWGIJIBoWhFtPchyiraDQ08jDmN4iOEjylS01+NlAIAo6P1+23xXVQf7I16oEknNdDoWHeoFtJsdrFCWEKjPl9T3zwNqyCyxo/qO+74v8NJLvSMK1i3VZPVXm7w5EllEWy1jDYEyyP3pp7u/0zBTHTazjsRx39jS64q2I9oODj2NOMhHtOvrd3mnnAP9qGhXojKAaZZmtofQbqJ9A4D3q7/fD+CXxvfvI4mDAaxXFpPfADiSiOaoIMgj1XcOHQLHFdMkkiedouHThuXLZYWrVasm17Z2wyTasV/OXM/3gS99qQ2NagdcMKSDQ19AK9pZHu3aWN3322+Kdm1gk841pEUQAqjEUvyI+0XM6TMURrSJ6CcA7gSwBxEtI6IPAbgAwJuJ6AkAb1L/B4D/B+BpAE8CuBzAvwCACoL8DwB/Vf++pAMjpwLRN5Ji+5E6dZbzaH6VdZbzBA3qbTE1dxrW7dw1NbMoZllH1n7w3wAAP/hB0S1qE6ahdeRPfwIuuqjTrXBwaC0iQ9EOY3sfNjocp9bvdaSsIzEvkPQKggCI1SxyxFxDh86iyKwj72YWHWFZVwD4OLOdKwBcsbHtGRoawurVq7HFFluwVRM7hkoFGOqNtEmSKE+d3CZEO2MTeYm2EAKrV6/GUIfOnVa0B1HJHEDUrv4ZgG8kjouexzQMhnzd6+Tnpz/d2XY4OLQSKY+2sPdh1bE6M43C3herUop20GVcYApICT69f3n6EtOmlN2OO+6IZcuWYeXKlZ1uSgJtjXj09mXAjjvaVwoC4IUXgO23B8qdG31XKrK9JYQo/SNGI2sMAn08AiVE8B+131or1TGPlWqolezMc9UqyeGEaG4fGRoawo7cuSsYuoObibFEUbChBnmcHbx8rcU0VLQdHPoRcSRJdpZHO0W0+0zRroUZCnC1Kt9z3SbMNSBFtDmfvUNHMW2IdrlcTsqcdwv23lt+Bpu9DqV1DKP81KeAiy8GvvY14Kyz2te4Bvzxj8AxxwA741k8+7e1wF57pZY/9JBcPhOj2NpbhSXRLhO2EQT1Y37bLvfjF8/sNWEdADjlFOCqq4Af/Qh473tbfigtg44RGkQVcZairYh23yna05BoC9H1710Hh9yIQ2F4tO2kszLWv9YRVtFevRrYcsuOv3fzwCTa3KyEQ2fhDD1dgMrefE3bj/78SNyItwIvf3kbWzQRunOK4VlDm/VXZQSImPzYRvA64ow5Lr1etyvA2uLiI4LI6OACyAPpF6J99/o9cS7Om1bWEY1K79frcEZhtXkAACAASURBVHBIkFhHPIGQmZWrjhtEu8+sI9WQqeWwerX8/P73i2/QRiIVlO8U7a6EI9pdgMoue7DLfrDsrTgONwKlzk4+aCIdwbfm3dOks4SQTe8X1OqddFaH0CtEW5+TEsJMRbsKabXoF6L9ngfPwZdwLh5/no/Y32or4PDD29ioNsERbYd+Qhwpou3zgXQm0Y7D/lK0qwFDgfTLJ6PoW60G/PWvLWzYFJH2aDui3Y1wRLtA3Hgj8O1vN1+vKnIwsA4nlW6Fol0bjyasb0Pw8GMAOj62aAp9DD6iTI92BTJYs1+I9jZlmfjnl4/vya6zahVw221talAbYapHDg69Dm0d8UvEB0NW6gJJP1hHQqNKOfta1SsND7Pb+cxngFe/Gnjssda1bSpw1pHuhyPaBeK444Azz2y+XqVmvwypzHVdQrQ5RTtFtDlFeyyYsP4EhCGCJ58F0P0FXlLWkYz1+o1oD1EVAPDnFyb68CdgzZSzcXYlKmsd03boH0Tao+0DobALJJVxkVq/15GqYpzxHgKQGYfy0EPyc9my1rRrqtBEmxA7RbtL4Yh2F6AS2Ds4c+TdaaKtO6SN8mjnIdpBkHiau70yZNo6wj9KiXWk3PtqEAC8FMhyzLlSY11/fcGtaS/Gv7uIXXb77cCTT7axMQ4tx4YNdXvudEAcmYo2Yx3pM0XbfK+w7xgzoIjBjBnys9OzXOnsV45odyO6fHK+dzGZhAwVJiAjrMVIxkIFs85jjgF2200mOLEhZR1p5tHmrCNGhTE2FjIIEKrbsleIto8ok2gnirYfoR/GtisV0c5V7rfPTM3jTy9nlx12mPzs9pkYBx477ijdAtPlGsahSu9XziDa1frf/aZoN7WOZKBbiLZu6gBqrmBNl8JdlYLwj3/kX7fKKNqmApyP1UwdN98MfOc7/PJUMGQeRdvyppqsol3wIW80TKItQGyDE6LtdfnIIQeiCFgVSqKdq1Pv9tHSJFHhgqdM6Dllh55DYsn9xCc62o52QSvapRIhYqwjKUWbeZzjGHjkkSJa2HqkrCPcuCEH0dZ10jpNtE2Ryyna3QlHtAvCE0/kX5ezjgSjtfp/usSjzSnaE4i2hXQG44aizWUd6SHrSKqDg8d2znWi3Xw6stuxZg0QqxkLrsBFCt1+ESeJ8aB5Kpzw1tuKb4hDsbjkkk63oC1IrCNlDyEzE5lH0b7oImCffYC/nvGjIprZUqQVbaYPm4yi/bfORkOmLIyOaHclHNEuCBs25F+X9WiPdQ/RnrSibWlv31tHmhDtMjXvvLsdL71U/ztXhHu3X8RJgntWTTxd60ylUocWwpser8Y60eatIxWDaHPp/e6+W34+850bWt3EliNFtDMEH+sPDMycKT/Hv/2DFrVsakgLPo5odyOmR2/SAUzGmloJ7Vb5lHWkXYSFsT+kso5Y1hGR/K7kxZIoW9obVCanaPecdYQh2kkwJPWIov297wE77GBdZAaK5VJPuv0i5gRBHsc486yaeCwjv7hDj2CT6XEN47iuaLPWkWr9OecU7V7ytKezjjANN/vydeusqySKNma0qGVTg6loc4Mlh87CXZWCMBmizVWnMq0WbSMsi+xZFZoVrIlrsq0DXggBDyK0EW1FxhFkKtr9ah0po0cU7Y9/HHjhBevbUx/iICqsGmT+zHYf9CJKpIh2DuvI8Ghz1duhuxHNnN3pJrQFUQjp0c6yjtQMoh1lM2rKTHTaHZi0dWT9eusq2qM9hpktatnUYKaZddaR7oQj2gUhyW2Z477nFO1wvAOK9nPPWb/WSkbMWUcC2b6yCviLarx1ZBBVvkPoIaI9WesIRd2vaIchcBH+FVUMNLcIMcGQ5mkw/Z29DJ/kzcg9q+bgIm5CRhy6H2Mzt+x0E9qCWEBlHfHYbFEpos1oBXpGsxeQyzoShliCuYgy+nX9zHda0XbBkN0PR7QLgibaeax+rHXEVLTbxDqDLbezfh9Vs9uSEG0/nri+3nZFrjOIKm8dCcO6daTLy/3mtY5ooh1bBh/dhkWLgH/DRfgqPpMZ9JrVqZv2xvFqf3QxJUW0x0O7op2aju4Vor1oEfCRj3S6FV2JsaHNO92EtiCOofJoAxGT7bdSa24dQUWm3ug1RZubKF6+wsPLsQRn42vsu1d/PYpZLW7h5OCsI90Pd1UKgibaUWT3r5kPeDcR7eFR+y0RV7NTDYpAtrXky4O1KdqaaA95tXyK9lh3y6ETrCO2axTHiUdb5Ihk7zR0qqpV2NJ+nc186dOIaPvKOsI+q+bj0eUDxASnnQZcdlmnW9GVGJuxRaeb0BbEgpL0foCdeFaNlJbca0iMyo5D9EAwXqoEOyP4jIzK76/H25sS7RF01s8/4T3k0HVwV6UgmFPmts7LfDFXIsY6UukA0R6zTx82VbRDrWgrom2pIFaryu8GvWyPdpJ1ZKy7i52krSO+XdEOw7qiHXS/ol1Wgm0NA83TODLqiXlvj1X7w6/sKaVuPLIr2imi3SOKtgDk1HgWli41kktPH4wOTjNFuyzvA1sXVg08DEESadaj3UMesTzWES0UbcCm7ExltxDt1HvIWUe6Eo5oFwQzGNLGkVM+Vu7l3aZgSFNxHx63k34d7Mi1RdsiyiVlHbEq2ibRzpF1pMsVbXPKDqir+ilEUU8R7YEB+dmMaE8360ioBhUVxjrSbURbCODRR/nlIyPAPvg7DsNidp2lS4EHdj4WeOMbC2hhd2Ns4GWdbkJbEMeUlGAH7AJJNfAxw5N9MefRJiH7isKtI6tXA69+NfD001PeRB7rSGVcHsd6bNZU0R5GZwNn07OM/SFs9Bv64y3YhWhGtPMo2u2yjpib3jBmb0tej3ZJPee2DjtQivaQnxG0kbKO9I6iDTBEO6Vod7+lYKAse+0AZet1TnfqjKJdq79sx2v90fEHQj4X49GAfXkH4pazcOutwN57A0uW2Jf/+tfAo9gbf8ah7DZ2311gPh4A7rmnoFZ2L8a86ZHeLxINRNsikFQCHzNJ9sUcMRXt6tp+9jPgr38FvvzlKW/C9JlzinZi/WRS1QL1d1y3KNouj3b3whHtgmASbcZRUF+XUcnCaioPUYtalt2WqSraOuq8XOI92rWqXDZYykm0a91NTM20SkDDOdIIw8Sj3Q1KZzMMQBZJ4oh2LutItX7d+kXRDmI5YGCJtjG4iLsgA8OKFfKTyUyWa6a/UlG+3ZL9mPsZYznSOPYDpKItUFLdfsquqFANfczytXWE2ZDqDAv3aG+2mfzkbuwcMEUgjmibZedZ64iKW2oL0WaygQGN6f36o7/tN7irUhD01BPQXNEOmTRpOniQ3UiLkIdom8TZlhtZJ/5Pso5kKNqDpQwvWRAk0e/dbrWYtHWkCwhYM5QqIwDyWkea37fjQfMCL90OIeoZGSqMzcscFMddcNvqoFau25iME23VNvtsfIN6DKPThWjr9H5a0bZki6qGPoY8OQDnPNpC9f8hk7mkZchLtK+9Vk7bWBAZ7xUuVqhiFOlhFW01KCn8mG+6CdhlF/lpQWqW0VG6roS7KgWhMlJn0s082mEOwlKkR9sk/cOV5taRwJIOOg5zEO2aJtoxS7RFzThvXW61aLSOWAcGYZgUNOgFRTtaL4l2gLLdix/qokMZ1hGTaPeBdSTXoNhQ8bvhOmuizXUbkxm3v7D5vhvfoB7DWG2aEO2Y4FG2daQa+hj0QvgI+QIvwiDaRZaJ9FV/smEDv44QwDvfCbzlLdbFUZDfOgI0vIfN7ah3Ipd/vGV44gn5efPN1sVp64ijdN0Id1UKQmWkTkxtM08pTyfzsJsqWZEV9lKBmTUmvZ9BIs1p8mS5Ihd6CtJGkrVHuVwWrBpaGTOm9bqcaE+wjjBEew1kBoNuIGDNEI7INwynaCcWIQSsepKyjmQR7dHRnqjdnAp05AJAjXiKjl/nahXjn/osAJ5Q5yHaW84cBQC8MGu3VrWsZzBtrCNCZtTJ9GhHJQz5AXyK+fR+JtEuMkhBP4xZivYzz2RuIm0dsfdhprVq1Rr7OimiXWQ/tsMO8nPZMutil96v++GuSkGojNY7G1uS/5SiHTMl2A3CYlORW4UgO0U2gHQHbGuLnjrMyjoSaTW0RGzQxsiw4XXtNeuIbTAURVgNmZO34wQsB8IgOxhSp3HMzDpiKEBjNWZa9cUXgU02Ab75zY1scfFIDYq5Y+4mRXt4OKlWl0fR5ga0Ww9IMvNCMD2qJALAAMmLPRpMD196LKSiXSrL+9rm0Q4iH2U/gg+eaGsPBjcT1jLoF2eWon3HHfJzzhzrYn2/l8CnmTWtI6NjXApAg2gXecyDMsYHzz9vXZwqWFN0WxymBEe0C0JlzJieqkxkppN9eddqrWtbI1IJ/BmSYAb6WRVtHSSXpWhHeh3BkjSzU4u6vPBHHutIXKsr2oIjYEEAzJ/PegrbCU20WY92YCrazQeItZDpYtaskZ+XXroRrc2JBx4AiOpTsJNELkXbJNqdvm09LyHaeRTtVHYjA1tqoj06PVLdAUDZk+diLMwg2tUq8I1vFKt+tAk6GDJL0Y4F4JOAT1HTYMjCFW39sspQtEcefgaPYQ9gzz2ty/W7aQA1XtE2qmFygo8+VxH8Yo9Zb7uJoi3rOTCF0xw6Cke0C0K1ahBtS+eVrk7VfAq+fUTbvo5JnGvBRLKhVbwk64iNaCuSUioJdoprZMTcZ3crwAnRVlUDbVlH1q2JZTEbZCidq1ZJMviBDxTSzskgVNeNTe+nFW2KcmUdYeM/Sd1DL7009cbmxeWXy8+MgcyjjwJXX21flrZ5Nfeld1zRjqJJEe3amJ1oe5AXb0Vl05Y2r6O46SZZer4JMon2174GnHUW8F//1cKGdQaRUrT9chbRJniegEeCH0SaRLsdivboKLvKO689GXvhMb4YjZphHkCNFblMRZub8dHnqnByq495+XLr4lwVih06Cke0C0Il8OErS4F1Oi5HgFUY5FAGW4A8OYBTHu0M60imR9tYJ4Zn7ZBNRbvbS1knHZynUltZrCOrVhvHw81T6gCfsbGWtm8qCNW1ZRVt7cX3YnawZBLtMGSCp/TIMWsKuFXQlQ1n84Ul9t4bePe77ct6TtGO43qmG846YjxbXLBXGHmpz77AscfK0vMMtI2PI2kA8KUb5uMevIpN+9ZLiGOCjxglVRmSJdokpEc7D9FuB+nMwO1L5wIAVldmWZebinbMBUPW8hPtohXtF1aW8RrcheXY1rq8bh2JilfXHaaEPupBuwuVmo9ZkKNuW8qklIrMKoN1YmZTkVuFXIq20QGb02oaiaKtYoiyrCMJ0bYw9lGDa/aMdUTxZNsU4+rVxvpc/6fPQ1cQbcM6Yss6oo6x7Me5rCMhl6WgyCmaBjyxfBN8FWcjmLnZlH6fJtrMOqk82j2gaBuD/9o4Q7SVANADWSlbBh3gm0W0z/3rsTgI98gYgx5HQqJLvEc7Fh48giLaTLaodivacqfWVeYMyX50ZdU+ExOa1hFm4GwmBeCIdqie+Qh+oYOuy259Of6C1+BSfMy6vC74xE7R7lI4ol0QKoGHTSB9EDainVK02Qp77bGOpMtH29dpah1JPNp5rCMkOwRL5zQyWj8XsSWItJvQSLRtebRTijZHwPR5aEMHueLOp/FmuhXP//5x63I9rdos60jJE2zgTbcR7d1/ewk+i6/iL0u3m9Lv0/EUzOyTkSGo4+85g2hznCcVc8Ep2opoc6pfr6FSAe7AP7HLhQBCVQGUPW/m931BtKGsI2qAwfTbiaKdJ71fuxRthty+bEDmtnypah9Ya0tilke7EhhEm3kP6e+LVpHnzJSZoNbBHiuRvIc84Yh2l8IR7YJQCUuJop2q8KiQx6Md1rpH0TbVWltVuUZFO7T4q5MOIYNoj46bHVx3S2mJkuDLP2xWl9Vr5XV7GdbyQk8bp6C/+smluBVvwo8usEew58464sfsNTTVXVYZNIm2tnYUAPOc2+7JPMhlHTEV7U7ftrkU7fpBsYq20ES7tc3rFM4+G3gt7sAj2Mu63DxONuWqebvPmNHC1nUGsVDBkFnWEZjWEUbRjjugaDP95pxB+d5dWWOIdlgP6Oae50rNFHwY64g6VYUT7SFJtNdijlXFT2ybvnDWkS6FI9oFII6BWphtHcmjkpnrBAX6JMNUAn/7OlETv3iS3i/LOhIDhBi+to40I9q9EgypfOk264jOwboVVnYF0b7/WamKvHJve7CXbgrr0U4KE/Gd+qQVbZ2BpACkBpFTzMue61lNebS7xzrCp+ucjHWkPxTtJ5+Un0uwq3V5KuVhHqLdAzngm0Gr1Ul6vxqjaHsCPgn2vOj7rDsUbWUdCXIo2px1JPCN9Tmi3R7ryIDKhLMWc5g+Wc0y+s460q1wRLsAaMU3sY7Yso6oh91DhFAwXtc2KdopBZIhRuaoPtOjPaCi1xmi7SGG53u8daQiz4WHqPNe1yaoW0fkMduCIcdUcOdsDPMErI1E+4G1OwMAvBIXgNvMOqIVbcG+YMz7KcyjaBf4Ymg10eYVbTOP9pR20zrE8aQ82uYgwYQODOz0uCE3brxRZrNhBm5bbCEPROe1b4R5r3DXOXW79wGhiRorQ1qtI8qj7fGKtg6YLTyPdo7I/U1LimjX7FaLdNYRzjriT1h/wnbU7osmt7pYMke0hTrfvk8Q8AotbucwNTiiXQB0+dYs64h+MQ+hwvs+wzZZRwySMOX0ftqjrYRSW+eUEO2SxwdDKqK9KTawHVy3ILGOKBXfmkdbZ+mguCsU7dWRLOLABvioplQwZFdPdLGHEv+CSRFtbibGJNoF5iNOkaccAzfbNcqnaJvWkV5QtOvXrVZh7gXRY4r2hRfKz4cfti7ecjN5IVfBXoAnbeebHkQ7sY5oom3rt5V1JCu9n565KlrRfnHNAF6Op/A4dmf7TaHa+FK4uXW5PkbZJzPWkVxEW52zgu0agXrfrsUc6zHXrSMqzWyXF3qbjnBEuwBoDjEEybitirZBtFlF2yC0hab3y5GaLLeiXc6haJeyFO0yyqhhiKpdT7QbFW0r0Q5jEGJ49myGEmGIUczEInywbbPRzYj2OGbYs45of2MpzlC0m+fRro6GOBK/we14baEDjckq2rb3pX6eZSU5LnC5uUd73Tpg/z0qePjUrxdrO8jj0a5OQtHuFaKtT7xnv0abz5D9cT5F276LfiTavlEZkg2G9AR8j7eO6Jmroj3a1z/4cizBy3ERPs32G9pmuTJkKkOGAh4iqdAzFKga5rGOqM+CrSO6b1mHl1n3o+18pYzsVw6dhSPaBUD3MwOQb2gb0dYKWJaibSppbVO0myT5B7I92qUBb8L65raldSQjGLJawiwag09xz1hH9EvKNmUXRwIEAc+vn6MJCAL8M36E07AIf/tbUa1Ngws0TRKgoGTtsM186QKeNdNKYIjVnKK9bEUZv8WROAy3dz3R1s9h5rOag2j/9rfAQ/8Ywnk/nAusXdu0LVNGFDXPoz0JRTtLoB8e7iK+2YRoe7Vsop32aHOzjMwPOoElS6RV5qc/nfImkowiKhjSGsSepPcTfBVjnXO9YEV7kOTDWMVgU6K9IbZnhYlCAR+RGjgw1hGDaLPBkOrrou0aNVPRzsoEpYPyHdHuOjiiXQAaibY164hpHWEuQ56pzFbALIzDKmCmoh1MbK+eLk882rYpSIHmRLsmibZHouPvsWYws6gA9g5OxEIes0eZivb1OFFuw1JdslUwRVQuA0czYpoE3mSUbNaks4SALcZkkrsXX8xu98ZgstaRZkQ7Ro6sI8x9q9NAcmkRW4Y8Hu0cRDtKrCP8a2KXXYD99uuSuEDdCIZoB6MyeKZvFO0HHpCfV1015U3EosGjnWEdyePRLlrRHiJ5DTlrG1An2tygII5kn+yT4LOOhKXk72YebcDeD7YKgSr6VcEMu6Id1cUPYOqxKA7FwRHtApD4lSHf0HZFW640iCpvHTHUwCL7dNNfykaVpxRtm3VEfmYS7RjwEWUS7ZHqADbxxlTO1m54e/PQ7/Usoh1HsRpc8FPwo8PGIGZtcUVr0iSaIdrGIVgzEGjriPalWwYGmnTOwDhPtI1MFytXFTeINO9D7gVk2sVtHEE/e2UEfDxF0FzRLpHcUOGZGUyPNqPGpSq9Wq4zgKRfylK0166V5ev/fMvIFBvbQjRRtMNReaFXYBv78jwpV7uJaOvj3Ahi26ho2wUS0zrCDGLi9ijaA3kUbd0W5r0qYtUne8iwjpQwCEnqWaJt9OdFVjFOzWbbPNpK0c7KfuXQWTiiXQDMFzPAKIPq4RigABFHtOP2EO08JEH72oB01azG32UT7QbriC0YslbGLG+8t6wjqoMTFkNyHCrrSIaiPWJwlOq68Ra3so5UUF8T6whg9+4minaZV7TNGAUu60h1vL7t9eszm71RSBeSsd9PI8PZZDwJekXY1KNN4INe/ZeWyzahVGwArOnRrtgDTVPBzRVm0CXye7RXXfO7ybaypYgi4OPPnIUn8Qppp7AgHJM3ZssU7U4nGM9LtMOQXUcHQ+o+zGodgc46wltHwrg9irbuw7KtI/K+5QbFcSRnVuXxMNaRqIRZnhQ9mgVDArCm8G0VtKJNiJmsIw0e7S6vPzEd4Yh2Acjj0dbZGwYoQMiUsg7bRLRNQsUq2lGMGZ4c4dcsvts8Hu3EOpJRsKYSljDk1XrLOqLUIFsHF2vriE/sy7syZijaBRLtVEY91jpSv/62l65OJZVMU9pma9TvBlFl76eqQe4KJdpm2j3mhTn6/Lrkbxsxjc0CF5x1JMcxl0IVHF10loJqjAiqwmHFXoHTJNpsMKTIJiyp7c3hfc+XX15oYhkAwOOPA99b+U68HdfzloJx2d9E8K1el57zaOcl2rvtBhxwgHVRJLzJKdrcQNNM71fgealU5bNVxSB/nXX+d5ZoxwnR5qwj1aiEGb56f7OKtvF3gXaNIMxWtJ11pPvhiHYBmODRtk3B6ywdXpTReRmRzwU+O7kK1oTADE8ej5nMXyM5nsGMDjtRtPmsI3Esg258r/uJdmId0UTbYqMQyg/olYhPJTVeP1dVRl1sBdL50pv7Dq3qlp6tyUhpGATAAKooU5jLo71uQ4HFmMxAX+YFNPJ4vUqmTZlKqmEiZElaYKj4LO9RJ7dooj0+ZlzncYZom1awahNFm7klU1U3met89dXAhz8MfOUrWS3eeMycKT9fwtas0qnTpXLnP2Ud4SxCZl/e6Q4qD9EeHweeeQZ48EHrYp11JJNoK0Xby/A0t0vR1vFBFQxlWEfyKdqeB3Y2ORJeUiiGm1k1B9RFklttHeGCLpNgyDJvYXToLBzRLgATPNq26Wj1cJQpkh2/BbrDANLTVK1GmmjzASR6hG/LOpIcsybaXHo/Epl5tOMYSRWyjhf+aIJ61hH5acsqogNvMhVtk2iPF6iMmESbUWnCpkS7QT2xFmOSpNSnOEkR14iUoj3c2RzxKUXbQkyTapgUyfvWQiSCQIAQS9WbyzVcNTzaBVpHxo1JEVbRNom2fZW6os35ck0rEjNDore9ZAnT2BZBn/NV2JK90Hq2hkvHlqtgjRnY3gtE+09/ytzERI92k/R+3ABEPedFe7QrNUPR5gZUTTzaiaLt88cTx4SypwbGOTzahQZDNrGo6D45iRVy1pGuQ0eINhH9KxH9nYgeJqKfENEQEe1KRHcT0ZNEdA0RDah1B9X/n1TL53aizZPBBOtIhke77Eeyc7KoZGHsJb5otpR1C6CDoUoI+PR+kcCgH4IQp3KMaiREe8hP1reto5UEAc/qaY5i+f7wekDRrmcdUYq27ZiT9H6efHlbrnNa0S6QaFcMv3Ie64hlJkao9md5tPXgokQRr2gbKuq6DfYXYitgEiPuBTQ6YgxAMqwjJT0othXpCeTAOquoh7ZoFE1GTKIdVRmPtnEuzAGYiVDbT1i7jLEucz/Nni0/N2zgWtsa1Kv08TmNA5NoN1W0+4Nor73rcfwE78Lq7fa1Lk9KsCvLn+3UpT3a2aJQuxTt7GBI9Q5iZp9MRZu7t2OByRHtIq0jQTahN1OuFt0Wh6mh7USbiHYAcAaAA4UQ+wLwAbwLwIUAvimEeCWAtQA+pH7yIQBr1fffVOt1NRoVbXv2BsM6wr28Yx9Dyq5RaDBkre4v5ewNUSQ72gHUsvNoD6pOztZhi3owJMDknRZUz9na5f1FvWCN+r+FaOv0fqTtMpYLaRKj6jgje7cAqUIyzHSoOaCzKtr63s6wjuio/pIXs4SlWq3/vX6kTdYRTtEerf9tJdrqGMtelqJNkmhDsDMXQbU91hFz4BYzQVpRCMyADPayWUfiWA6G5d/NqyRyL/dNVCrj4SWrmrZ7Y5A6nZyi3aTAiPkzVtGudRHR1kGfGcT2h3fviffgJ3jHmsuty2Pt0U7S+zHreMhWtEV7so5UarKzzUzvZxJtW9EtJX74GdaRWBiKNpci0xARirRrmO9bTtgADOuIU7S7Dp2yjpQAzCCiEoCZAJYDeCOA69TyHwI4Qf39NvV/qOVHEDFh5V2CPNaRJMDKi9kXbywo8YkVSrRV5zqAGm8dUZaOQaoxinYOj7YAPIiEaFsV4JjgeypQpcv7iyQbhSoUYM06Yni0Bcg6T1+p1P+uMn7ZVqBm2FJsL1QgB9FW92FmJTmlGJUoZhVtk2ivGy5Q0TaJNqNMpSqjWhRgPftUyhoUByKHoi33307rCJcNIQpFUrk2sFznPH7llKLdpIrrhr89WajSmbokLVG0GUJpBo52mmjrDijjvI5Wpcy5QdiLtyQ5sjM92oZ1hKEMbVO0FekcxuwMRVseMzeg0uKH74M9nhTR5hRt0SZFO8om2kl6Px0r5DzaXYe2E20hxPMAvg7gOUiCvR7AvQDWCSH0U7EMwA7q7x0ALFW/DdX69hD3LkEuj3asrSMx2zlFgpK8oUUWrNFTx0OovmTDIQAAIABJREFU8NYR4cEngQEKGKItf5cQbYZEe1RXtK15p9VUpucVe8ytQGN6P/vAQRNtpWg3I9qVCYtbBtMiwPE8k2jbLAUTsstkWUe8iPVo69MwA2NYP1KyrtMKpFJXMkpPyrtuVbT1oDhiZyXCUPrSJdHOth201zpiv9BxJDADcsVadeLylF85R5EezoqkD3MDNi10cGGeTlu1UqBFHu1uCoZM/DI8ydOBdB4y0vt5IjtbVGIdyRiAiPYQ7Uog+4r12Iwn2mZbbAJWkgkKbLpOSbTlcXQ8GNJQtG19WNInZ4gfDp1FJ6wjcyBV6l0BbA9gFoCjW7DdDxPRPUR0z8qVKzd2cxuFCVlHbMqg6tAGfF4li+J65HOxlSGN1GRcNS2lNA+ihlpkIdo668gQbx0xS7Cbv0mto6cyfVFoAGgrkMc6EkeQHu28RLtARTuVxpENhtx460hSec0TbEBSVQU1bYMVWDdaINHOYR1JnRdrsJFWtPnZJx0AKjMzMPtRbeGIXqswXjUKaTCVRk1Fu2axcecp3pLnfkoR7QJz/JmXZP0wQwajbKKdSu/HKtqmv6TDhCYP0VYkjRs4RMKDB6MEO0e0tXWEy7rTpmBIrWhXMIMNQExZR6zp8FQebZ8yFe2Sr55Xzjpi3COdVLSddaT70QnryJsALBFCrBRCBAB+DuBQAC9TVhIA2BGAzrn1PICdAEAt3wzA6saNCiEuE0IcKIQ4cKuttir6GDKRFKxRlgL79LoipqWYH3kLmenA3GYRSOcAtt8SkgCDVbSTILkhNW1nae8E6wgT4a7T+2VVpOsGJBWftefc0uCkClkW0TaIUdWiLrYKKWLE3E9mgRlrYFSjos1klyGIbI+2Itpb4yWsHyvnav9UEObxpZtZd6zp/XQ8Rcwq2knVU49XtLV1pPD0fuPNC2lEkbK6IEpSE5rIpWgbwbXsDIn6PmuqvxUwT+e69Ux7TaJdcHq/FSsyGtsq5CDaOniQza4hlFVPW0es/TapAHWwMQp6QB2gXKyibZRGTw16DAQi2zqSCAE+2IxfMTzDo80p2h4GdPXITlpHYpnxyMuo5+DQWXSCaD8H4GAimqm81kcAeATAHwCcpNZ5P4Bfqr9vUP+HWv57ISxD6i5ComiX1IjYohIkuS99wQZtmLk8i1W0AQ+RqnxnXyeG7JAHKGAUbfmpFW07SSP4FGUTbWjrCPWEdYRUhw1w1hFdpMfPR7TbZB1hiXYTRVtou0wG0a4HQwreOqLEzS2xqlCibT57XLrI1HmxEu3mz2oyHZ3l0a4ZHu0iiXbFULQZv2YUyYFBGUFTRZtTQ9NZbOwHXVe0N2ubos3P1qh7tol1ZABV1lKQJy/7TTcB224L3HprnpZvBCahaPNEW9lCEo+2ZR140qPN3P9CAIGQz3Dxina9P7ElGQAaiHamdYTYgUPao21vSyg8lKFybRcZDBllK+dJZquSnk3uano0LdEJj/bdkEGN9wF4SLXhMgCfBfBpInoS0oO9SP1kEYAt1PefBnBOu9s8WSREe0B+WnOTKlI2UMoOhiy3yTpSQggfURNFW2DQq6FqI9r6mGeqTi4zGFL933Jeotjo1HuCaAuQlxHcmcc6YhLtWoFe/JSizZAR4/pnWkcGMnz2OhjSjzOsI3I/szBacEadHMGQuYl2hqKtg14pQw0N2hQMWcmhaIeSaA+gZuW/KXWXsQuYiiJ3OHmCFFuBFNFmLAVhM0Vb3SsDqGVcw/r9xOUff+gh+Xnzh3/erNkbhxZYR7SwkTu9n+XcpQoXFR4MWVe02etserSZomgeYvilDDIuPJS85taRAU/FUBVIbvMp2gL+gCow5RTtrkNx5sgMCCHOBXBuw9dPA3i1Zd0KgJPb0a5WoU5G5Kfdo60CrEpCqQBjE9aRD7IaVRcZDGkUGGE92sKT6f0oRC2aeNskgYGZ1hEdDJmRd9pQtIMuJ9pCIJ2u0JZ1RKsn5eZEexZGCrWOmGnc2JdHRCghQIgyQ7TzWEfqU7MhQ1hqodyPLPDSpmJMU1W0tc0rIdpMoSXE8Dwvw6PdJutItXnasSiWKTQHRA01y+AubR1Rqp+fHjSlFO0mHm35gzYp2ozSHDTzaAcxAA8DqLF2GTPrSK0GzLCss9128nP5knHL0haiJYq2yjoywKuhdY+2/d41T2Xh6f1M60guRdtSgEqn98tQtAWQBENmEm3ia2W0CuYMMqdoy3eMI9rdClcZsgDUA8YycpMmHm27SgC0T9GOQgEfkSTaU1S0hZA+sbrXj8k6ktej7QuW9HcLzAI8+v+NqJNxvux8Ram7m2JDsYp2qjKkfZ0w9pIgOZu6JZRdRnfqmen9PIFQ2Mfy1cDHgBfCQ1yoFz+VdYTZkcn/YouPoq5oZyhgaubCz/JoBwbRLlDdNWdIONVP58UvI0AtzCbaXHvNGRLWo22QobDSJkU7j3WkmaLNqfjG/cQ9q3omczm2y9X2KaMVRFvbQnS/bdlUDA/koe5pbjh3qTSPRSva0SSJtjW9n7bzEf88o65os1lHTGtnFyjaXokXPxw6C0e0C0Bio/C1x4tPyVMu8Z2TzDpSvKKtA7l8EpnR6ZmKto7k1oUPuGBIM72flWh7Rrnf4vIrtwJaSaAshV536j4fSFSpeRhEBUOoJOm4ikCKaDN9cRgThkjK6jYBMlGrM6Ypk2DIjBLH1dDDIAWSaBf4XkhbR5h1UsGQlpduMvvEW0dEXLeO8Iq2+kS5CzzaBN+XRNtGkico2rbc4aaizal+o/WgA3P9ViOtaDNBcgnRtlsK9L0ySDU+jaNxP3HPqp60egHb52r7lJGHaJuDC8vAIRJ+yjrCe7TBerTN01S4oh3V4znslWvV9UWWR9vMOsLEXAhCWb+/M60jimgXGQwZZyvaSd/jFO2uhSPaBSBRtFV6oNBGWLSiXVblyC1VEiPhYcAvXtFOpp4yKn/FkErzoBegGluItshDtJsr2lo5zyqP2y2Io1iVV+ezjsQxSR83EfvyrtSkijyIatsU7ZBJnRjGXkK0rX5NrdwO8Iq27vhLJT69Xy2U97ZUtNtkHeGCFI3nM8s6kija1mBIo6wzl4NZqV41DLTFOkKIWdIZx7IyXglhPqI9RUU72lAvu1kba5OizeT0NjPq2NIeav/1AIVNVW+A92jXhuXzsxzbFXqdc2UdUcGDtr4nyZpESJ5ny2sonUe70x5t491j63smqOtWj7ZI3lVszAW85tYR+JliWqtgWkeswkbkPNrdDke0C0Aj0bbaKHQwZFlNJ9vyY4LqARltINo+8XaNSCnNA17AKtpk5GO15cBOCtaUsrOO6PR+HOnvFggjeh2wK9pC1IsjCJCdaAcG0W6Xop1JtFX+d2vmGKTUE+t9m3i0CSGTPqsaljCoiHY7csQD4LOOhNlWCzOegk/vVx+sslk61LUdx4yC82h7GMK4zCLEZgORiraPKCnkYkKftwFU+WqYeYJrh+uxJ7Xxdina9mM2A31t62iCPuAFGfnS68dpFhIxUVsus89uyCiq0hLkso7wOaWTOgAUGx7tidsQStFO0vs1I9qFKtoD9X1ZFO3UoDnD5qVTrmYFQybvb+b0SiFM/jYu0jqSUrQzZtMyxA+HzqK7mUyPIiHaJUWiMzzauqqgLfBMV2P0qFgykkyleXFmGijfExjygtT0nUbiRdY+MTYYUgY6AozVQtTL/cZdfnvGobKOZB1PXD9m3jriG0S7uGOu5UnvF3sY8niirQcOiXrCZFqRijb4rCORj0E/hF+0dSQVDNnco51ZECIrS0GiaFOGR1t+VjFkncFqFcZrPmZgXA5iOKIdEzyfUEJoJRKTtVGwivZ4/eS2jWhz2WVMr6tl5iJU137QC/h80SbRZgbF1ZUbjB90mGhHBtFu8ILpn3meqA+cGy6RnqXzMjza5n+LzqNdjbOtI/oQhzDO59GO67Ov7AwVTOsIIz6ZinbB1pFSUmXaHqBupvfjPOUOnUN3M5kehe54fE/AQ2QnLFEMQlwvm2p7wetiAhnZQFoBs5IfR7QjNX045NcwbqgKyTbiOun0EGUXrCnxifWlZ1B27Fwn2C1IOrgs64gwLAWcdSRoD9HWirbPkCsACGMfQ36Wok1qmjJHwRqf99nXohIG/CjTatEKmJXuuBemWbHbGmykc96X+GuYxzpi8pxqpbiX4XjVwwyMw0eUkXWE4PvaOmJRtHVgIAW5giHZ6XXjfNYqxT3L5v65qfNminZY04p2hnUkRzCkmd2nyEwrkybajKLtEQBfkrlGS5kmbSmi3bC/tiracRmD2tpmIZ36EIeo1tyjXc62jpQ8uX37TCUQw8+sldEq1KJSEqDOWfWcdaS74Yh2AUg6MJ9kbmob6dTT67r0rWV0rqsxZmUDaU17NdHmSYJWtGd4NYzHNqItCRg8jz9m0WAdsRFTEHxPsH7AboKIGqwjtoj9mEBAvTgCR7S9WhuItvwcQoW3jggPQyo3bLZ1RAUcZXq0wVtHRFlaRzKydLQCZnwEl3UkbEK0zRLHTaejszzaxs/GC8z8lijaJDIVbV8p2jZxXefIHqCAD4bMQ7TNvNMFKtrms5fLOmK5zolH2wt5Fd8k2syzmkpc0yWKdgyPV7SJ77c1aUsFQzLWkQGqFe/RFgOY5fOkM1G0vSpi+BCBXdGWAomXoWib1hGbDVJ+toNoB8JP4mbs/S3S1hFXsKbr4Ih2AUi8bz7gI7aTzqg+qgbsD4ckt+1QtFXHk5HpI1JpoGaUOEVbHk8W0Y5iFeios3RYj1nldc3I0tEtSFT8Zh5tapJ1JDSItqW8faugFe0hVFgrUhh7GPJ5oq1JtD8oiTaXdUQWrOHT+9XiEgb9KDNLRyuQto5winZ2kJyZijNuFgzpZyna9e9Nv3yrMa6Ca32KWXUrilFXtC3nJVG0Nem0KdpmcK1FFZfbab+izeZLN72uFqKtp+XLHl9EzLyfOA5tBknG1QIVbd2ArGDIiE91V7eOoDnR9nnrSEI6vbD4ypBxGbNKvKJdJ9oqvzWTDi9Vgp1Jret7MmWtbfZP/2RA20MLzaNdqh+Ptb9VM6s664izjnQdHNEuAKaiXSL7NL2OFC7p4EFbx6/TKpEoVNFOfLceeOuI8KWi7ddQsSjaehvwPKXAW1SAxqwjlg4hgp8E3nS7oh2HqoPzsrOOJN5dVtGWHekg1VANCiTaiX+xwltHhI+hklwxsJCnZNo1q8BFDDVYEryiHZfbYx3Jk3XEDIa0VrpU1pFyRjVMwzrCPUPmfkw1uNWomIo2S7Q9KQRQ3JRo5wqG5ALGzCwdXR4MmQSo+1EuRZsjNCbR5ipztgR5FO2Yt47on3skALL79dOKtn1GZwLRLlAcCYWPIZWJK8ujPcPnibZ8VgX8LCtY8u6NrQN0/ZNymbeXtAqB8DHD04q2RcxJrDDOOtKtcES7AKSsIxQnhRJMiBzWkaR4i1dwMKRW132BiLkltKVjhl9DKEoT1JxE0aYMu4yQ56NZwRpPZx3pcqKdZBTRnnNrcCdA1MQ6EpYw6AUYpKBQRVsTgBkYZ++nKId1hCDgDSiljCPa2qPNWkcGMFgKM4lpK6DbV0aNff8HRvaIyPYchnXrCJBBtEkH8TZXzrnUcK3AeCCJtlS0uWIbMutIiSIr0dZENAkMtBHtHOkiU9aRanFkJFcwZBNFu16YiO978lQaNYMkQ0te9pZBN8CSHztpi6loM9YRX2W2smWgqRNtaurRboeiHcHHQElu39Y/JWKCmpWzxz4hCVAX8OwVfTXRRmRXtEOdNSz9/yIQxHVFm+t7mvXJDp2FI9oFIJdHO0oTbWu6KfgyAwfx2UBa1d7mwZBSaZ6hOrBGj2nKo52laJPIJqaQXvDEOtLFRHtCwRrGo92sYE0UE0oUyxzllqqbrUKQpGyrZVtHlKJtI0+5so4IgEgS7RCcdaSMwVIkiWmhwZDycwA13pfeRA1NrCMDGQPEGNKLT/x1Ns9nkaRTB9d6xL90Y6E82hRZ74VE0fYjNhhSk84SAj4zg0m0i7SOmEGvXGXI2McAquw6yXX2M6wjpp+fI9pGkGRU6XAwZGx4tLOCIaFJJUe0m6f3G/CiwhXtWBAGS3ymjwlE2xqsLWOFfNXV2lPrymqYHtljSPRvBgaUdaRARbsmSnWF3nLfJoKPs450LRzRLgCNiraVdCqSpitysen9kiqJRab30zmACbGNaAuRFC2YUbIT7ZR1hPG1TagMaSVpXu9YR5JCP/L/NmVEKPUkq2BNLGSKrbIXWWc/WoUgIJRRkyomY0UKhUy7B2QHQ2qPtr3j14o2X8CiKsoY8LM9za2APoZBVDOsI6alIDsYUq5ju87CKLTEKMBRexTtOJYDfN8TGR5taR3hFG2TaDdTtIdQ4QsgmenwilS0jWvCEm3hYVATbWvQq9xG2ecrgHalop1JtJsr2rr/svXbWkH1PMBn0lvWKyGHiOGzmW5agcjI9GH1aCs701BJ9WG2Sq/KwugrDcCqEqesI5Z2qAFUomgzRZJagUAFjgPcADFdRMwR7e6DI9oFYCLRtq0jmlbY05k+ZCGZAhXtxDoCu3UklgqP7wuWaKeCISmyF6zJoWjroEtumrKbkASRljIUbZ3S0Ce2YI0MElWDiwJJZ1CTJbel5z/DOqKJNlN0yAy84T3asfJo26eSQ1FCqSTakt7PQ4QSIj6/tZlf2WYdiWWVRR24bFe0KU20bcGDBqEvMhjSTCnJkc5IqDzajLUtyaPt8xk40sG19ra0zTpiEGc2X3rs19OkZQVDlmK278mlaBtEu/Me7RzBkKQGkjQxMDad3q+JR1tXQi7wkGN4mfsJKrJtmmhz6UdNwcc6cIaZ8cuiaKvrOqDClYrSg7QrqF6B0i5sSOuIyzrSrXBEuwA0Em1bP5ikQNP5iC0PRxIM6RWd3s8k2v5Ez58m2l4Ook0ZKr4ur55ZGbJ3rCPCyB0O8NYRIlH3aNsyVgjUZy4K9OIHISTRzrif5IuM90DqwkTJADGjSE9Sstw2uIAH34OaRSk2vV8JoSr6ZF8niLwkN6/9xazTOGbnDm+uaNfPebGKtlK4fMGqW5Hw4JeAkhelSpNrJIq2Jp0ZWUdmYJydiTGDMQu1jhjZYtgS7MLHIPFT8OnsMoyiHXaXoh3Bw7La1uwqusBLDB+ilkPRbiTaZtYRrWhzHm3tnS5Q3Y1QqvdPtjzaeoCo0+5ZBSxp8/IZop2UpteKtqV/0op20cGQ+tyWfD67SVIZUqVcdYp298ER7QJgEm0Pwq7uJun9eOtInLKOtCGPNqciJ0Sbt45MyaNtLfBC6YI1XUy0EwKmjseWdUTbKLKCIeOYkjy1RZLOIJBe5SwrUiyo/sJk8sc292jL+15WUixZLTWR8OD77VC0JdHOykUfxB4GVQBobEkq3fis2oN4lUqWQbTN81ko0RYqLoCY65zMUMlga5uNSPdHg6UM60iSxabKz5CY1pECj9mcibB1GULIWZRB0t7dDItQxgBxsop20UT7U/g2dlr1N6xfz7QlrsdINOaUTrKOaKJt8es3BkPG8CdUNdXnbbBgoq0JcOLRtolTurpnmSfa2s7na8EnsCv0mVlHtEc7CYYsZhCZVJnWRJuZQSQI+IPOOtKtcES7ACQdmE+qrLmNsKSDyuzBkHr6qmii3aBoT0im2lzR1kqnJtpW20GS3o/Po62DLrNynHYLdIrGesEabuCQTbQjVXa+6AwctRoZ1hE+6LXsy/yxptVBI4lwT6wjE7cRx/VgSICZpocv08v5xd7bJtHmZtjDyDOCp2wvsvSAig+waqZo+/AhT1iRBQOFyvrCDtADWe3RLxFKXnYwZKLuZijaQ17VOigD0gpwsdYRQ9G29SvqcgxlpElLFO1ylqI9cZuNqJnpIi33fssQRfhvfAAAUKlYF8vqhdqXXs22jtgEksQ64lOiAE8gpmF99gMojmjXc1fzhF63pazyW4dMxeUs64hJtD0S1jz/+lyWE6I9uWPJizrRVgMHNiYmTkqwO+tI98ER7QKQpE0q8dYR7e8tqVGotdNAuxRtXWyDIYNRlBCjobJcluXR5jIZ5M06ogNvut86goasI7ZjRg7rCMH3kJnHvBWoW0fsSqcQypvo8YVMkqwjSg2yWUe0iq+j+ieoenGsrjPVU2wV9G4II20d4W05QewnKQ3tWUdyKNoxkpkYlpjGXlLVrlhFW9lYmIwJqNXqRJtiq6Jdz8DBp7oLAsBHiDLzvANtVLRTQYoWS4EOis28zkrRZoL+5HYMEs0S7fr5LFrRHsMs1S5LO4x0nsBE0l9P76ezx0wMjE3l0S4xxFQR74RoF0w6szzaSVvK2mrBWNtQV7S58yJtm/Z+Y8J+CrrMyeBC2WViWyrCBvHDKdrdB0e0C0DjiNj2oIpGRdumwgg/8WiHNu90q9qriQT3gplMMKT2aNtUfEW0E09zk/R+RaeK2lgkWUeUMmLNx5qo+PzAIVaKdvHWEaVoMyqyvr2yslHodIUJ6WTypXskrSOAhWwYA7fk3BVFtLWineF/D4xqmNZUnMZAFGCmbzW5baJoJ0Q7KO46p3LRZynaPlDyGKJtKINZ6q4+t1wWG5P0ti3riOUa6hmE7OwN2qOd0zrC3E+1oH1EW8M2Q1In2ioAtKminWEdMZ7VxoFmYqPQKnK7FG0b0db3bZINhM+j7TOxQqkAUMZyVs+jXSzRriva2i4zcR2hEyuU+PeqQ2fhiHYBSDqwkqcUbd7rqhVtzjri+0bhj4JIZz14yh5VrhVI3wNmDMpljVOVsYp8zvJoRzoYUt11jR2CqaiybekiJEqC7uAs/Zv2A3q+x1tHYp3SkNqkaMP68jAHiJyindwrpWakM06I9oR7OwzrRFvfCwWNp8JQpbqjmC31HsT1aphZRDtb0TbsPxxJEx5mqny4hRJtbWMhZuBmDHRYop2kNAQfDBmI+gwJly4yQGJdKPKY0+n9LO1Q32XmV46kZcov8TavXNaRVBabNhFtSxabhGh7srOOawzRTjzaGdYR5dEGeEV7cKBYRbuu7vI2iolEO9vOB1gU7RTRts/+1ffDe6dbgYRoexnH3CgEdO8rc9rCEe0CkAqGZBTtxKOt8hHz1hHUqyQW1IMlmSSaKNqeB8xQnWm2R9v+4k06OIZop6bsesSjLbOOZKi7cd0PmGUd8UgdcxvSOJb8GKGYWBjHvG85RTvxA2Z06knWEa1oN6bMM4l24m+f+nFlQauuHtkHF4Ak2onSmZHzPvGlM5kMpEqWoWjHPmaWJOks0qOdtIVTtPXA2YdSoy33gkFYBLwJAXCAPIYyApR8vnJtFIrEulCsdSRb0U6sI/o6M0Rbe/HZ9H5RfeBgi8kAGqwjlnSRrYJ5TcLxiTdUVTYzqSqYZZEAYLX85bKOaI+2Ip2FW0e0RSUrSFGl3bNWXNYFaxixIJVphanKrAcXpYJV5LwebWcd6W44ol0A0un9mBGxTjKvC39kVYbUKllBPVii2HFKTmIdAWYM2ol2Ko821zlBBoc2I9q+JzJ9kt2CRtIpLP6HWFBSgp0jLDFkOWy/4OIt9QqgsOZLTyva9qI2E9RdJusIqYEDYHnZGYpqopIV6HFMrCPMuQ1jP8lkYD0erRjpF5k164i6zpkebR+zSkrRDgu8zoZ1xHo/GVYwOejirSOaSDRmrAAMK5IH6zYAef5nqADEQol2Ku3exGtYrxjIW0eiUGT3g5AzJEnRG846EnogKGJUoKK9enQo+TuoTLw++nzP9OxFeurPez0YsvGZTwVDNvNoKxW5qEGkvmaDOawjA4Pp36TW0f2T7sOY4E7t0bY9Q+22jiSB5daMOum4GUe0uw+OaBcA0zriefaAJP1wlIZyKNoZAUn/n713j7W1OevDfjPzvmutvfe5fhd/Np8vmEvsRA1tUjeCQkjBBRJKQ0QBUSGVUhBNgyJHSG1StYgmqSIVteFiEpeLRYkUlESoNSQBhGOTKAICtcGBxHaDMb58vn23c923tdb7Tv+YeWaemXnmXeucs2afc+z1SEdr77PevdZ7nfnN7/k9v2dX+0s+wRtdRxZuPyV7v2012lsx2p1j0lp2GXvQoKxEKIas2IppRF26BLSdpMYdd8vCwDAgE3NuK+ffKLF5BRAlQjRJ1azUyN4PeMiM9qA80JYlXACwsiY2uKhIR/gxSxOZkwh5X/Aqo93haOaB9rLlgio+ZwPK60wLHa2ddERivbfphklSpM5MuNgMNjCqj4J0ZKp99zhYGAye0a40WhoQgXalG+ZybQKL35LR/tCNJ8PPq9NyX4N0xMhAu7T3E6QjiY92hQEO0pHGjLa/J4nRnpSOUH+Kmr0fc4uqMfRaU0Z6ooaBJCqNiyGnGO0oYayPyft4uLEH2g1iuxbsSKQjdUa7vXSEM9rTxZATjDZptIEJ6ci0RpsP/LVmAo9ShM6QE2AxOq14oF2zAPSLC6AtOzJl48htKTs1YCUy2mqzdMRLF4JGO2f1CGh36gKAtpeOaFQZ7ZXtsCAANlFPESaybTpDVjTaBx7QLwXrxF3FaOEzYVaWKzEf7a5SyBit7vzvwjHH4to6o71eK/R6QI9lW6DN9k9qTBQL6aYAi02yNdKieD1wRlvel+WgcYgTt31DoP3/fua1cb9Oy3QBAW1i8WsabfOgGm1ikUmu0Uqj7b93yt0kAu2pGhJffN7LYDxntMWGNRfMaIf7ViI2Mo32ntF+9GIPtBtEUgxZST0VxZD5RGZt1FE21isXQDsfwbi9n89WVjXaQN07HKl2V8Dz/u9tvZDuEQpiiIN0ZMpHm5xWRNmBk9vQRNYKdG4C2uH8G6BXg9jtb5s0ZXAd8ZOdJB0ZoV0NQ+NjXq8Vcx2paLRtF2wrJfCUd8OU/N8duMWkX/raGhz2Lq++ugDpSCjMzJ/nRDpiZb2+PxF0DSUJBOnfpxltt/CetQbaLCOsH2A5AAAgAElEQVQo10q4V/JXll1HsgWVMPYkQLvGaA8mAG3J9WJX8VvPf374eXVc6jUKfW/NLzq0YBekI4HRVjCV7EZuddeM0c5tBKWMm18c9fPK2AMgdx2pAW2lVZ00uiBGe6v71m7OuO3j4cYeaDcIzgzWurO5dA9YZ8hsA2sxoPOr6sYabQIS/RSj3cEYC9V3WOC0Ih3xjHYF1PCiP6AcKGMmILK7LRmhBw0Crso3ChAXVNa3YCfvcLHtvPIgrW3qb1ugHYohRXZXhc+o7Wu09/OTXW5x9lCkI/L1AXxrbuokV3Fa2VwMSU4GdY32CI1FT4z2BWm0p3zxNepAe02Mdn2BOIxe/95B/Az/VTB6bA60ObioyX8ABlgmiiED0yk1WhqcXAaY0GgPXDrSDvS896XX4QpcS8jVcclox2MmvfgG1xGBIOEOHLHR2MNltOlZFYsh/YJrNt/AaMNGWVS2Ddm06omGWiHjM2s7fuWLJVG2NqaZ1b105NGLPdBuEIEZ7D2jXdNoq3pnSBq8jPdXvhDpSG/EiTnsi1FA17lJMxvXydINmLBEIgBQAWmPu3Sk2oKdWUlJAyVptFsXBhZAe12feDtdKYakCnf/lsQAx2KjOqMdpSM6+e5dBwFtPeHokjLaUw5B08WQzt5vqgOoxoH/nrbSERU0/5ukYEbXGG3PchKQEFuW+6xcVz+3w+B04DO1anrMiXRkCmgTuytlYjJ3GfGYR4UZvItHVTpiLkQ6cnt5gFfhU+57BOlIIZfJ9oXOCdWPSJ7RYjFkRaPdnNEmjfaEXCOA/kW9P0XMuHmSKz8vvO18xSKT5qX+ony0u/r3xM6QbcfSfdx/7IF2g0jt/eRJyIEe9rBnxZCJTqy5dESltlY5AAtAG0Dfw2AQ5m4VNdq1AivoTDpSL4acKsJ6VCJ3HZnUaFeaPQCITXr8eWnGjlBdQMXRJUqeiNGWpCNIFkvVAlBl0VU0kHa1hoV2nVO7tizMQMc8pdFGHzyAJxvW9PV6CtLZTzYmgkZvRiiMWK5lBngXETSoFUbbDiNGGJhugtEOQHvC55/OrVFVRns9KhhtPdBuqdG2ob29dA3zZidyx8DUUUe8ziPQ0/fUGO2xY9KRduPXaGNh5qR0xIO0wl2DSfUAJx0pGO1tiiEJABOjXZHUPGgUGm1ROuIB8GLiGvrng+aY3IOcz726YpEZaxjajl/bAO1tirX38XBjD7QbRK7RrklHEkY7G7wobZlotJsz2lq0oKPByhgAXScCbarkBjZIRzRrzZ1NvDRAGObZ2qrL2C4iSEcmWNkR3vZtSjpCIE03HrQt+WhvYrR9a25Bo31P0hFitDPpCHWoMwbNWZhh9A1ral03xxEr9MGaTLJGDs/HbAOjvcF1ZLCu5mKGVXONtlKqymhTatwYB7QtdFkvSUBiPrFAHJ1Lh5lktDnQbsloW8Y0y+Mt4FrKu/2S9jWVjkiM9jACM0XNjSakI95Sr+X4NVgdgfZJHWh3NS1yIR0pC2OTYshN9n7edaSVvR/VDQTmXALa4b51z6pYMOkzbv28QnIFFt/LNoV7OzLa/vdW4xe5jmzDaDeW4e3j/mMPtBvEtsWQSoEVQ8qravJXbspo24zJyVr10r5pjcho5/vLpSM1FsBLJKrFkLwjV2VQf5SCJAVbFUNOSUfIXaYxuxuLXn17+5rriLd9W0mNTKyrLYjSkbpEqJaaDQu3TjUH2uMIZ9lW67q5XmOFHn1nxRbU7jN8xmfmGe0JJwNtKgXFQChubg06o0ZbZtf5Ir6rTOCxGNIkf5Ns489L1ymsUdFojwpGAzO1xqop0EYE2ttIR6aKIae0+CPQK89oC/eTtcBgzWQHyl3FaGNh5lrw0Q7HTPKGio92kI4ImchEOkLzQ2WumlHxczNGO7X3E0xhYjFkcB0pz7/NiYBqNll5x6/SIjOw+KQFb3TMWzHadm/v96jHHmg3iNxHWxyQvXSEWLL8YacBmmyV2jLaKm2rnfutZox2hzXWBTvCiiFVBWgj1WjnA3I4ZqMeC9eR4F9KIFogr4JGu5tgBoN0pH1hDaX6RUabMgoTHdGsX1BFhxQZdPKGEAXQJka7U9B9Y6BNi0h6hgRP6TU6dJ2tW3Fuy2h7i8Yqo+0XVL1aJ226dx026MXlBXpYOBuFLmSX0s+IzOBEJmaMDjRVRnsEOjNipttrtLsJScdW7g3E0E9d59HJqvhnJvvhTzV1oGzJaHOgLTHaQS5DQDuXcPldI9cR1//g3jXaoUhxXmeadxGxiRKgMIrgtiyGFD4nIwJyBj4hfKg+KrvYD811ZMLeL2RiKh1L9/HwYuPIp5Q6VEp9v1LqJ/3vX6yU+ob2u/b4RnhQN9n7celIrldmuujWjLazbGPOGKtpoG0wlOlD764B1BntwepEOlKwaLwA9DGQjlDKblI64gf10KZ9SjrSnNFWk37pyaRadctxC6p4DSUwnmogCybN31/6ghhtYvFFvfJ6wBo9+s4DjSnXkQ2MtpNr1KUjIzRMBw86G2u0lQMJIqO9ZgsqIxewRY12/b4dBhUaE4mLGADrUQdGu+XiYlj7zAUGuUB3G6CdM9qS68io0Olpz3UAmHnmPCckdhmj1Zgbty+rMynj4F6DF3pFo01EQae37Ay5idFu5TrC7tu6KxL8vqR/k2zjx+SqdIRbGtYWq/7X5uQILXjvgdGW6mb28XBjm5HvpwGcA/gy//snAPyvzfbosyD44KQrGq/RptKR3N6PyzVqDOTO9tcqB/prjDYb4IJ0JBucCo12pRjSTPloS56twkD5qETsqEnSEWEbq0NrbmCDdKSxxm6TX3oAwL4ISHLLcfftZteRRDpSydaYTsM0B9pe9qHlxQXd69sw2nruEMso5KxtKIac0GjDQGuFmVqJzYB2FcEusgYSgnRkitF2rx25N1RkFEYNrmENOvEiDqPLyPV6wHJot7gYfFfHqcUSwF1Hys/IMxc1z2KjxgmZkXudmXrR5a5ihIoyirMJjTYtinOATEB7giBJCgMrdSb0+9y3PW/NaGsC2lINScY0ixptuIVo1YiAWxqS/KpmENC5GpBaYeyDRjweX9AqjJM2l4402pd93H9sM9p/obX2BwFnHmqtPYGTae6jEqRvjMWQgtaVmMHKoC4y2g2lI4mBf0060qnIaOeDLddo17xHM3u/nD3hi4vHwXUkb31bGwQ1A9pWQKZOu2vj4qJxMWTNLz1lcmRGO7QvpoXDNkA7l44Q0OsQ2cNGC6pCOpI9Qxx0GsF1IfkM38VVbFiDDGgLTWKCRluvL8Z1pLIvYeHcba6X6KcsDUcEF6Ha+DSM/t6uSJF2FcRo10BPIaOQ7P2o5sJnLmr2flqheq+E7yHpSCN2F3AEDnlKSy3Yt25Y429FEWjfi0Z73pjRDplV5dj3qWLILTpDRunIFKNtp+VXnfaLrgc5snqEosuJrsGhoVZg1x9dcupzNbYB2kul1AHg6Eql1BcCXhi2DzGIxVCdqTbKcD7aNhZD5p7Sy8gu1uzYdhWhU1YF3OaMdoe1wI7whjUQi6NGaAeiNzDaxiAwnY+FdIS6Pk50wwzANAMs1gIWrkj0whrWbLBx1GZT0yF/nbCW2UPPGIX6g2oxpI6LlPM2VgVDkLpYGXQGvfjEMROjPbEo4MWQokRltcYIA22U02gLhaa7ik0+2vF5jrUQNekILS5qjDYtnNfoRIS1thqd8XUbLYE2Z7QnNdpUDFl+RrC/nLI0JFJC8Jzm3zPz8pLmGm1vS7k6l6Q9fl9CMyaZ0VaKfLS3BNqVcXvWmtHm2unadS6KFIXPKaQjE99Tc2hKZHbtFpGB0SbpiCiLUmmt0F468shFt8U2/wuAXwbwGqXU3wfw5QC+s+VOPe7hGh8MUJ3xk11l8mYs8jovMAmsk2ovHckY7ULLR5Nuh8BoF+k2ptHuTMVH2+rJYsjHznWENJ0eLFZdR3SdAeY6yfbFkNMabV6MWpX/jCqks3QFPFExZFU6QixynwPt2YMcnhhO3jAhHWEsmVa2Yu+noDHE61PV2as60F5Hp4+ZXmPVUEYxsloIUaPNai6mgLbGEFl8SfI0qpTRFnzdyNLQaItlU6DtGe3aPRmAdtxe2mZz0asrFHaLss2Mdkvp22gV5mR1N+U64nX4Y60Y0j/mnR4LP3RRo50z4wHc+ue9lesImxOldvF8m02dIZPxKb/3OaNtZEY7kEK9vhCgHcmpCmmXNBHbCw4etdgItK21v6KUei+AL4WTjLzFWvti8z17jGMcRmgowBjv31sBLLxLYuVhd+zuxTDagVGtMtpcOjKl0YYsl/Hp9TBoZANC4jryiGi0z8+Bj3wEeMMbyvdGCxguHam2YEeV0eZNI5rb+wXpyIbGRLrO7pLrCOAAbFUuw1Kzxf0UpCMqduFbtqw/qHdXpUWlMRMsmT9vYSITpSN6UjpCzLk2yklHWjLamGa0ub60myiGdDKKadDZKRvtItenxTaD1ZPAdFcRpCMVico2xZDUgCcUvYos/rR0JNdot5SOjNCY+06jEqMdfbTda1Wj7e/r+5eOeB9tD25becRzAOw610rnnyRPMogGAEsabV9/UEhHuI92xYggWKESo30BuvQp+1GOJfY+2o9ebOM68i5r7UvW2n9qrf0n1toXlVLvuoide1xjXLtJCsbEyS6L0D2vwihdfDFk7CxVFkPGgphpH20C5FZm8ZEy2nkaLOmGWRnULzq+8zuBN74RuHOnfI9slaZcRywDPUDJDHKd5JTbwS6CNOVdr0Qf7XD+O1Vt1ED3CjABTL0UZlPXU9NfANAO8gaZaS5Y/Kp0hHfDrGUu6sWQAdB3yhcGbpNMvL8IVoM1kMBdR0JGLfsM0itPdcMMGu36+DRY4xdustRiVxEZ7Q1Ae8KOjTzXDenSJRafdd2sdfwFonSkOaPtmXNROkLsLsllcskHy6YBcm3NVkCbOja21mjz+1aVzXUAhPqJyevsr2FfMSKgY1aauQjVSIleo2tYfxDPv2fOKxptBQuvANoD7UcwqqO9UmoB4BDAU0qp64gFkFcAPHsB+/bYBrFB0DraA41jRFsgYIrNVnd+MmzLaGfFkDkDuYopuxqjPVqXzgMmGG0GAAAUVeOJj/MjYu/3S7/kXqVuZ4HpJNcRSXZADWsqQDswIwqZdGf3jGeQjlAH0NU6qWqO7MlER1MbBwKNGgPswUjNI57qDzodvV8babT5+Z8shuyU6CMMEIu5JaNdk+UQo60dCGvLaBO7vkE60k1ptDNv3gnpiOlUXaMNg86s/JhwAdKR2n07WABqQytrYvEdSqu17zak0RZcL2Krd+8G0pjRDsz5UtjXYQRg4jFXiiEJoHXaYmVTSMCBNmoabb/N/KDOIu8i+L50lYVbkLEs5EUBIBAB2f5SwbrrYVEphmSMdqdGrCs+8g8a3Amqtogk0k4p7y++B9qPXEzRKv8tgL8C4PMAvBdxfr0N4Mca79djHQ5oWy8dYZMdB9pM3wiUhSoJ09YpxxA31mhXXSKEYsiCBbBRuzvJaGtblY4kqcFZfaC8yFgu6++FIrkJ15FN9n5BOmKQdebcvV45VKezzAUfABIrL1OXwvQbpCNUjBetK+V723S6OYsfwGCtqIkxzVOLiwdmtJlcY2bWuLVe7OLwxOCuIxsb1lScbgLoJMuwmoxCWy9t64qFG+DOudFLdEbW1O4qEqAtAeCVB50bGG3uLlM95n4LRjtIR9qMX1RETY4iq6VwT67TY57KpgHA1dkp7oxHCSeUAO1Kd9vCdaSR9C3x0dYj1kIDpMJ1ZILRpvEpl7qkGu1azwHK/mknY2nkER9wQFfXgidzrxpFW9Z9PNyoAm1r7Y8A+BGl1F+21r71AvfpsQ8OtBMmjUY8IPhoB3Y3f9hDAaKKGu1m0hG3LzQ45YM2L0IJjHY22Cba3YpchpiEamdIAgBcxvKIAG15YnbV3kE6UnMd0XXXkUQ60kUA3CICA09yjbzoNW/UUGkxHTuAVnSq0IlHfJkhiWnXC9NoVxrWbCcdUal3eI0l8245mzTaUmOQXcYYfNkrx8yabUwXQ7JFpLi4gPfF9wBsmXkNjaMD2sZW76ddRQK0BY0wZYmCvZ/o3pBqtMUmPbSgqhbOutdZR9KR+zueTRFrO4AeywmgXddox2JId76eWBxjhMHt28C1a/4zGNBWNekIscgHfmHdSqPNGW0zYr2cyCgQo111HQH6Be3vxPd0VszWkLMHSUeaMdrMiGCyJsbPvRp2Lx15BGObYsi3KqX+PQB/DMCC/f/fa7ljj3ME6UjOaLMIrblp8s7t/UIL9ouTjtDglOv9RKCdD05cu0vsobUxLwkCAJzRzj4jWVw8GtIRGl+n/EujdKTcV+sH9Zp+LqQgddRAttMrk3REds9IQKep+L+z61xzHcmLIeuMNtdoN5KOjCp1HVmnzqR8X6rdMLdktFVwHZlwN/EFiK30ytxJQm+h0Q5Ae+WkFeF4gnSkDjqJ0SZWfH2eLa+HAQOMs/erLGJ2FcMYgbbYaGmLltnbNazx0pHK8RTSkVZFciETZtHrQexAGdqR0+JiQzHkEwtXzPryyzLQxoYuxheu0ZbGp+D/XndACZnVXt7fwFb7hk6uniVNbXIfbbdwbiMFi1lGNeHyxOR8DR1Q9nH/sU0x5A8AeKv/91UAfhDAn3+QL1VKXVNK/ZxS6oNKqQ8opb5MKfWEUuqdSqnf96/X/bZKKfWjSqkPKaV+Vyn1Jx/kuy8iONCuTXY0SQFAh1UJtFlTiZrv8a6CmqrQ4JTLJWjgMR1iMWQ+2CbSEaZLp/dpUOe69LzdL5MuBOnII+IJOjkxe4AsDvyZj3besEbSpTdntDuZRU4KnzRk+Q+zcXRWatI2OpWO5NaVBDpnnNFueMxB3iCATu6Aoq3seT9iS422gvbX0Gb5czrXjtG27RiwAJ7qtqDJ4oLu3UwulsuiRJcOkghRF77TXE82Yo3OMdqmtUZbwaDubrId0FbYpEsPdSY1mVGQjrTVaHOQ3KlBriEpjnkT0D4B4IB2sQ2rp8gdNkLRJTHazR04lLeQrbuOREa7sihWMZtTSkfiONj3kAvHGSnUMkMVxuQJ6UjpBPVwyal9lLHN3fHNAN4M4NPW2u8E8O8DuPqA3/sjAH7ZWvtG/3kfAPDXALzLWvvFAN7lfweAPwfgi/2/7wHwtgf87uYxrFNGW9SGcgYYY6Fri57SKhYbtWa0PSOxPM+lI+6V7P06rEtNp8Ros434oE4De1U6MlHh/rBCTkG6am/a15XtiorI0WoodsyFvpG3+yW98nlDGQVvvJIXR/G2wmZCOhJaNlc02t7GMRZDpu9H6YiJnsXN7f2m3QNiF9e600rtGgLeXUYjWGTWsgV0bpsxYKzArZ/JICEs4nuVsNHp59jUU1rspKhg9Ijef0bhfOEZ7WAX2VA6EtxlatewAJ0ySHY2p3JROOCeieA6kpEJ9BkAJosudxF8QdXrLYF22awUAELDrScOIqMdtpF8tCvSkSkWeRcR79s6i0ySjqjRFoAp0vqo6tzrGzqt0JeLVcpE9gadHrESSJZdROLEVVtEZr0N9hrtRy+2GflOrbUjgLVS6gqA5wG85n6/UCl1FcBXAng7AFhrl9bamwC+EcDP+M1+BsBf8D9/I4C/Z138KwDXlFKvut/vv4jYRjpCumgA6FTZYS9OzK4d+UXY+wXpSJ7q59IRz2gXldp8VT0BtA2x+GpdWkkxvXJNdvCwYko6oozGzKzdgJwBbQtM2vsFBw5WDCm1ft5FFIxd0ZiIS0dw39KRkJpd+KKySstmbu/XdHGh3DFtdB2pgadtpCNeox00zfm5ZUWXnWCjtqvg0pFZD5xjXmfjeDHkMt8mK/St+GhrzWo7zvK0nAfa5EfcUqM9OjavyjQT6CQAJiwQYxdR/zcVNjQsHKT7ibS7BtDCOLmriJIzeOlIuU2u0c6PJ29YQ0D7xg32GRLQrpAFZmbQqTVWjQoDk+xfJStE+0b6a5kgca48tACp1Uc5RttOMtqx5qIN0C6KIcUu0xFL1MbkfTzc2OaJeI9S6hqAn4RzH/ltAL/xAN/5egAvAPhppdTvKKV+Sil1BOAZa+2n/DafBvCM//lZAB9nf/8cHnF7wdG3A3bFkLK+mrS7gJsgal0STacvRqOt4+C0TGWsiXY3FkMKn7EVox0L6YpjDpaGvN3vowu0CbhCO5utJWblgOxBZ5XR5sdMGsiWMgqFIG8oGO1VBrRr0hH/c9UOjzqvBelI+n7wZe9MLDxrtLgYRh2KISc7Q3KgLcm8uHREuhdIOkLODDmjHRZUDmg3l44YYDaDeE8mHfaI1asw2lPSkeAuM5NZ8ajRttWF265iGLwWX5WyNSBe502MtuHXuWLvlzDaeYaECIXOSTpaWa0lpIQaRXBL+z+bEbtekY6YLRltkvzVOin2Bp0amjPa9AyJYw8x2pMa7dRmtpCOsCxj1ylHoNQWq71zfpFkg7uIfHEhNhEDyzKqUSzS3cfDjY2jvbX2L1lrb1pr/08AXwPgO7yE5H6jA/AnAbzNWvsnABwjykToOy2Ae7pdlFLfo5R6j1LqPS+88MID7N6DR8Jok9tBIR1JQWep0WYpI+Z73GR/c0Y7dx1hLgVTQLvQaFekI4CfhKaaI4Q05QMe3I5iSjrigPboQE3BhqYNa2qFRM6D2YOaVoy29YCR5A0bGO0RumToE+mI7LwQOq9VNdqRAas5oOwqHAM5bpSOGGrSIwHtID/xvxdVvKNntFWVAU4aXBi5kGsXwSUF87kD2rlePEnBE0jONdpUDDnB7pKMIjDauXQkaLTrlp+7imFE7EC5oRumqvq/u6xcBJS1Y2b3Sk1SoFVT0Jm4jugBK0lGEaQj8ljKASUAXD90DEuq0Y5jciALKkXsujeOXW91zLxYXldqYgYLhTE6HonSNnffKgUYrKskV6LRrl3nrrHrSCFtq0hHWJZxLx159GLy7lBKGaXUU+y/PgngS5VSH3iA73wOwHPW2t/0v/8cHPD+DElC/Ovz/v1PIJWqvNr/XxLW2p+w1r7JWvump59++gF278GD2KApRntkjHYntFYdOetUSe/ubH+pUxYx2itZO82lIznwtBmjbaGTCZ4XQwKe0c5bsLNumDU29GHFVDEkjEE/wWhzG8cxX1BxFjnolVsCbSZRyVlXxhhVi3g9WAcoTVnRNGsEP+K6RltHRrupRptJRyY8pasgzUtH4mIpm8jW6wi0t2C0TQes7UbDp/sKvqCdzdxzWNgr8rT3pmLIqQJQ685Z72Ve09IR1Vg64t1ATIVp5guqSoe9oNGe0OKP1gGe6vcERtt9TzOrO8ZG96bSjpxkFJUC0Bxoz2bAEe5mjLb/nk6z5mqVOhNitBsBveRZNXWNPNWhKMit0fnc22O1nUa7Jh3pGi+cOflU8cjmlqt76cijGdWRTyn1bQBeBvC7Sql/oZT6WgAfhitO/Pb7/UJr7acBfFwp9Qb/X28G8H4AvwDgO/z/fQeAn/c//wKA/8q7j3wpgFtMYvJIRs5o1wCL8mffFUPWCwMD69RIr0wuEcRo564jub3fxmJIsu9bbmC0c9cRzlhUupA9rKj6l5J0pJMZbVt0w8wWVKTdZRptKWW9iwgSCCMv3EZmWVVzrCBZCIC6HZ7vTBhYpZqjTq8DGG/JaDsrrwozxUB/rQtc0VwqB53DwDpD+mtYnNuoS+8qspxdRAK0fXHz+Vklo5C4juTHjIzFr0hHuEa7VgwZNNoNpSO+MLOalWANRupt2tPMhewuw1xHJoA2dQxs1pqbu47ocZLRJqKmVohNxZCYz/GEunHv0hEOtPXYrHlLymjXnlXWlbl2nf2zCkDMOiSM9szZdeZEQMjy9tq7CDUuhuzcfSfbj/IxeRSdk/bxcGOKVvmfAfyH1toPeUu93wDwzdbaf7yD7/3LAP6+UmoGB96/Ew70/yOl1HcB+CiAb/Xb/iKArwfwIQAnfttHOijtSoy2lLImSz0A6LTAaDNtVgeZddrZ/iJltCelI1QMmQ9ONtoVGgbk6AbLgbZU2JEM6o+F6wjXaI8y8+HPbQ2k8WNu3SVx9LKPGqMdbRxVlP8U9y27zloe1Ck1G0BcrtEO9n4XIB3xMpbZTLmFUI3p7N3kKxcu5y4F2TEPg/OIn5COREZbo+vktPcuIme0AecidJTuLoA0W7Y+z5lBmzDahVwGDGh7PawEtNfo0HXwhMMFMNo1AEyZo95MdNgDuqRbr/A91jcmshukI8YB/1bsbsJo6xGrobyfgu3eLN03CkuyELosly7hkr2D47sjiIPj3xMZ7WxfEkZ72czqLjm3fHxijeAGNvdKWVPAZxn95NuhLN5MGG0qmFyOSa/e5JjNiJPmjHbdFclptGOWca/RfvRiCmgvrbUfAgBr7W8rpX5/RyAb1tr3AXiT8NabhW0tgO/dxfdeVGzLaCeFgTUZhVHoTFsHDkqlkQ9qLh3hTAI646UjJdBWTDoCpMxsCbRtUakdj/lxkY6oqNHuBlmjDQ2l4iSVT0KkxyYtPgCMjXoYBxlFVUfMNfIykEhdR1CVjiilmAYyfZ8z2iQdGRpJR4bRgf7ZHBjQYVimTVXSzpC1Y07Z3RxoU+0Ery2oFr32Hmhv7hV2X5EAsIXbl9Kuk8nSyK8+L4zNZBRSrYQrNGXFkPkCcRwxBI02xG6xu4rAaNeyEknLbNn3OM9ciJZ5VnvXkcq9wqQjnSrHyV1FBJ3wxXgTloaVduRJMxoAuHTJSSnOBwSgzX2caVwvNNr+3M46x65fCKNdOf8Z0Jbt/XSYe3tV12gro6N15dKmQJtf54bSke0717qftQKGRud/H/cfU6P9K5RS38d+v8Z/t9b+7Xa79XgH12ibrlKE5eUaAMQBISlYUo0ZbS9jIaCdTzBiMWS2K0nlMxUyMsu2qNF220yz+DG+VjoAACAASURBVI+edGTK3g9aVzXadhOjzUAPySiGhtdZK8uKLnNGW5KOnBWfEdKUVYYlFk5JVpABdM6460jDbI0C5nP3+/JsxAF7P5eOnE/o0mtMJ7HxirmOFLpo7jpCQDvrnLqLkKQjVSkY99EuNNoqLYYUi8rcAjJotDNAn8iijGp2zIAD/UbbOgDjxMUEo62VDQRp7kYBOFZeG0CPkMd1Lh25CEZbu+YtonSENNr3ALQ7rLE6HQD0/nsE6UjxOX5feoPeyOz6LkLUaAvSEef4NUenVsV1DpaGrD6qWDizY+4rjjqJj7ax7Z5n5m7SaYu10HHZInaGNHrE2KguYB/3H1NA+ycBXJ74fR+VGNapdGSQfDiZRlsa+BPpSNceaGsVB+Rl9qByC7RQDJnvL7cr7MrJW2S0MxYgcb3o5MnhIoMbbtRcRzZptEMBYk2jzbqd1QDwriK4ZxDrmncvXPPzX5GOIHcd2aCBxLooCOPti1vb+1HB3pzplROgzRc6W7qOFNeQd31MWpqzbbgsp1Ni2nsXwYFeANpnpSzE7YtmfvWbGO0JjTZJzs7lxQW1eg/ntts9mz9Qa/SK5Glg7g2dHkRGm6wgA9CuMNpUczEpHfEuEc30ysSoekZ7JRTX0jULGu0xvyc9c0u3swfaHFTKxZDZvvDCQD1ivWrMaPe63gguYbTPy3lKqBWalo5ERlvchooh0bkPN7tdZCT7Ysbo6c2eIe4EtZeOPJpRHfGstX/9InfksylyRhtwQII/zhYMdAqpzLQYsq10hFhX0vKtctcRpo2jYshSo60jWy0UQ/IGC4DMaFN6OgEsD1E6wpnAmkY7SkdqGm1fGEjnJD9mnhqkwkChOcguguRKQRdd04t3qm6Hx6UjtS58CdAusx8c3Eag3dB1RAPzhVwYyNn1KkuWabQLlmwd2eoaSKMshe5NZLRXy3ZAWwMzOubcF586vfY6SrRyIDHarZq3JBrt7DNooe002n5xsVq2Adqjgund/i5Fez8G0rSVpSPWEQBVLT4ii1/TaHMA3OkLKIY0Cr2xWIqMttuIFlyF3K8iHeELpqSGJOj18++JWu++YZfElNGun/9QDKltkVHIgbaTjtSBdu/nxJzkojHNzBijvV63A9qdRm9s7FLJniGeZazJ+fbxcGPjiKeU+lHhv28BeI+19ueF9z7nYxwQG9aQ3ddynQDthAGeYrR5wVJjjTbN+YW9H6uwnmK0g0abAMkGjXbNdWQqTXmRcfrhTwF4VXU/tnEdye396k16cAENa1wWpdawJk5kGrrzmRjJdcT/XG13Dc2yNXVG2xgw15FW0hE9CbQjuz4xeRfFkNl3kM7esG5zOaPN7OVcp9cOdnkMdfjAh5h+D3vO5lu4CEnZJ/c5KvGUlkDnYI1ntGWgHTJhBl5CZ2BXa6iD4qMeOAbrpD9V271Moz0I6XW6zlE6InwPXJYyAPpN/srrxu4yvnnLseQpvZ6WjnCJBIDIaEtuUYlGuwLYtWfXG3VJTKwTKxKhgYC28jaO+RwjMNr5ootnVsn+Ms/WcLlMZ2QLwF0EzzL23Sh2qUysdStZxn083NhmFFgA+A8A/L7/9yVwXtbfpZT64Yb79tgG91c2FVBDKUgAYnFOwmi3BtoejASXghwYsZRd1Ghngy1c6hZgriMC0DYmOlbkBTxJscujALQ/9nz4eaPrSD9OaLRR1/dKriONGG3XSMbCBEZb1mgbz2gDKGytRlZIZLTFiHJQp4Y1AGkg0/c50A6MdssCUK5XzgsDgyxKb/C859KR7L4NQFvHQroJuQw5GQxngjbhAUOUjuRAm7uOkAVjRToS71uB0fbsbucXiPn4lHTdpMze+e6PGaBiSCYdqTSsCRZ0EgM85kBbWkQyu8Ipe7+uzpzvIniWsVaMR4Cx1nY+99EOjDZbMG2j0U7awZsRq7FNoe8gFWsXRa+e5IKcUSiBdlm8ObJz21esdcN81jPpSIPJKhk3OKPN94WRXHoPtB/J2OaJ+BIAX26tHQBAKfU2AP8SwFcA+L2G+/bYhigdyUENmI+2ttW0HrkUAI0Zbc21ibKkIy2GlDTaWTGkyGh7WyUtsA1ZowBAZtIuKk7vMOnL6RJI6s6RtGDvO1tltBMf7SnpCDWsaarRRr0YMtFox4LWRPKU6AFRMNrW+qp+D9RFj3gCegbQc3fTtSoAHew0o83dQIyxdXs/zfyVc8ASgDaTjuRSDCoM7DUMnf+z1c69RxKgXWO0OetakaVRYWB8DiWphU412hVGm6QjgLufWvCdA7mB1AAwByw11xGLZBwsiuSokM4oGLVBOtKpth0DB1cCl8goxpGh5gjSCCzmzU5GkdG+kciICHQqwxjtygI9AO3G1pXuWa34/BPJhUqm2H8Gzb29XpfyEq6/pkLfiuWtY5rbAe2k1XtncSIAbQsw6YicZdzHw41trsh1AJfY70cAnvDA+1z+k8/tCAUZWsc0/TJP97DKZwF0Jox2a6BNzTa0s2NbZkxO7qPdYS0AbcZ0TgFtluLKWZhcg8e/+2HE6XHc/+G97yveT1qwdyWjHUBnwmjXiyGD60hLjbay0GQjOFQmzE7DVLqRJpInStOzqtEARph0pOqoY8D04q18tB2Anh+4HTpf1pnmKhtKCxQNKJQNOUSgXWO0e828qxtMzAQkFGtYkx8zb7Yxl9nonNGWHDhGD/RqPtpr3hhHcCLaZQyhY2OtSI5d50qDEbrO1awEY25rQC8snDvtbPcaMdph4TYleSLXkbns4CQVQ5aMtntNpCNCF1GFEUoBfScXZu4ihkxaWAXayRxTEgEAI3xUeY0SXXSwriyPGXDnLtRcNCgoSvTinRUlKpz82EtHHs3Y5on4QQDvU0r9czgXma8E8LeUUkcA/lnDfXtsI5GOVOy+Rt/4ACAHjjq4vRhG2/08w6qYYDhLA62dZVs+OEGFNrCTQNsfc6fHYuX9qElHbt2M53v4l78O4E8l79tEOuIHQcZo06CuNPPRrmm0O8Wat7Qthgx+3VP2fpUF4mh1Yu+3IgbY36RSarY4ZtYoQykqFm55zJs12mkx5LL4DErNig5BDGjXini5RCUA7QbSEc66Bka7yFCx+o8q0E5lFNMa7YotKLf3uwigbWwEwJnxd1zoGD/2bKHRzhdUYRwEzFgBeivnQW06uFR/I9CZLNA56JzFrFth75c9YpZlPwBEjTYrkhbrZoRxw7HIrmDvxJYWdLuIkc2J9UUxZ7SFOqB8fNJDoSnnTP8Uo60xQCnTVDoiuptMdOt1TbdUMxvNfdxfbFxuW2vfDuA/BvAOAP8PgK+w1v6UtfbYWvvft97BxzE40A7NQTL7spF7DQtG9MlKtlJgtbP9RfT07tVKYLSZRlspJx1ZW+BjH4ufwRvwkAc2L6oJ6UUvKRBSXMkA9whIR27ciD8PH32ueN8N6m5Am5F0JGO0AUy7jnAWPzDabYtetW+AVO1S2TF5w3mZpoyuI+VkF1OzxBgNQjMm90paV6Alo+2K10Izpqqn9IRlGGPxJWu4pGCp4jqSaISJJTttwIAx8EQsflWjPTOxe6ek0QaTjogd9ryPdgDaGRhZlhrtVoW+JB0J3T0r7G7Q1Aoa7cFn5eLiIn2fy9+qDZ2CjaDxjHYjGQVb3IX7NrvpCkZ7Yo4BEBvWsEMKYxhntMV7xcuETEk47CoS68SuklEYFNsXoftw7joiPc9cFkLZmvx55sdMjHZjoN13ctElL4bUykYZUR7/5t8Az5Xz2D7axzauI/8YwM8C+AVr7XH7XXr8Q9Joy8ygByNmxCpntDnr1NDqjssbAGCmVlgO+b4g7Avgik0GGOA3fxN47WvD8eQ+2gmjvR4BmMgkmFGQjrhXbiX1MBntGzfjIF3zESams+9Jox0bvMRBfcKDWZKONGN3fWv0St1AIqNgtpTFZ4Q0ZQm0c+mI0aXUgmu0w+c2OGa+L/OK1d3I/JVrLFmSmhW6uAYf7U5XHSuSxjiVRcwuQtRo551euY64xmhnGm25MNBp8buD3n9G+n7QaPesAVUDFh+IjHbXy6CHu46YSuMP8lwPx1y5b51GuwL0qDERFa+10itLnvcZGqTxlFjZmo82ZRkxnztf6ZWwTRfniGJMCIw2oryhodWdW7jVpSPGtyMXCSwp4zYhHQk1DIV0xJZAu8EEzRcXybnl+wLeRAyxziQ//3/8jwOXLwO3b+98P/cxHdsIyP53AH8awPuVUj+nlPpmpdSi8X491iG5juSgxgIpo12TUfBiyEZAG4iD7UytikmVd8Fy+2ScVZeOD/IAPV0MSRMDHbOyGFBnWJRyqblWPrTbxMu32PHVfIT9MUuMdtTxqSqjzSeP6MDRCGhDQWvuhytPvKZjzXPyBSIf1IXiwfw6d0rw0fbnwBhUJ+9dRGJ1d+iuZeEpzfTKVScDVn8gFvH6Y1aJdGSiuDl0m2ur6SQW/3yKxSdLybx7p007morFg75LYk06smYL9NaMtttfFRvj1IoheSe/SnOpmusIbyKmdcVnnknB+sr37CJ4EXVnZKA3DhYGa6i+g0LZsTEuyvxxKoWuUwkZwBduSjktds2hBkAVDO4igka7NxuKIWlRXEoyS+lIaUeYeJT7Z7Xslqyiu0lTRptd5wqgTwrUtVzQjZs33eudOzvfx31sjm2kI//CWvuXAHwBgB8H8K0Anp/+q8/tGFh3Kk0T2Xm+CmX2fqYE2okzQEOgzcEg4Az8C0abF0MC6P7id7v/Z+yUY4PGZDte1Ff4aAsFSbFjnf8eoTHORcaNW7yCv3x/tCqwJ7NZXaOduo7UZQcXIh3h2YL70GhbJhESpSMF0J5mtGv7sovgDGQA2pViSDOjLq4C0IaaZrTDNdT1YkhWJEfevOuzBhMzSQo0olwm12gz9wY966AxVIshJzXaXpZT12jTeMDkZM002sZ3oFRV0AlEoF0tevXPqoIAwDJXnmnpiEZXKV7bRSTSkdAMqJSOaIxQXsaSs+uhGFLHa9v3FquzIQxevBgS8E4euesIY3f7vh3QTjTatUXxCGjOaNeAtonuV7m8J5wXw4qF84XohUlH3Kvp3dgiNkXLNNrSecH737/zfdvH9rFVSbRS6gDAfwHgLwL4jwD8Xw336bGPcURoWGO8fdl4xmglawMbBHjQWSnI4NIRqSXwg0ae6p/pdcF8JA1rgMiCJUCbSUfIm/csjk58kgJIP1fRxvFBvQ25u1XcuMO6bwkss/PR9ox2X2e0t9Jod7op0Oa2e7Vuf6nrSJ3R1ixNWQXaoehV0GgLQLsFi8+79AW9cq61HCLQdk1VSjYo1WiXjLZdxRbsNaAdvyemo1uwu/w5o8XFcpUvdHxGoXeoMi+AA2K2Zlqj7cBtTcfKC0BrLja7igE6tHqXpSNMDqDrRWUhc6GG0paSyUKqGmG2TbB9azBwc+Z8ynWECJ+ZXhcLB8uKkim6XjuZxDve4T4jB9raYqgsygDGaDc45m002rnrSA1o0+Ki14PAerP6qErX02GMJEuQK7V0HZmQjthEOlJhtP/tv3Wv16/vfB/3sTk2Am2l1D8C8AEAXw3gxwB8B9DECvWzJpJiSAJPp2k/b9c9z094GiWjfUHtyKWWtFVGewpogxVDHszd++yYAwBIBsEc0KfFOQZDYQ13kcGBtgR+hzHKZfoeDmizi8T9ZWs+2kk6uqF0hLtR1MBtYuVVA9pWkI6sywUVZUiMKheRD0M6EmUUci1EotGeaDsveTBzFjMwwBWNtu5MeH6a2PsxpjM0rCk6vcZjxsGBA9pnOTOYAu188c27CtaOec002kHr2uCYAdJo10EPdx0xE+4NoShcD1jl19lr8clFZYrRdkWXaMfuSux6rtFmmdXejFhmjWQKH20A/df8GbfPL7yQbpMw2vkCPbK7oWCvxTGz2oKqvSIjP6RMcb64cNKRLtvGvepOB6AtZnwYo22h24xhHGhX6g+4VXAoBs6B9gc/6F6ffHLn+7iPzbENo/12OLnIHQA/DeCvwwHvfVQi0WhTQ44MaFuvlwXgNYNy+qq1RjsH2jO9xrKi5QvFkMTIsTRwwmgfOIup4SQWBvI0PuCZJ6sTD2aeGgRkx4qLjBvHPa7gFgA5K8jlMrOFwRo97MlpfJ9Ap+Ya7XrhTdTL7p7R5inTWqEp1wMGBjKTdFhw6UgptbDrKF0ACJjKiwu+AGnJaG8rHZnWaLufJW/eAG6ZzGtVa0A1MxF0nrUFYOT0dl5jtGcGeOYZZ9d59zT9HD951yRPgbk19Yxb4i5T0fzvKgZk0pEKo00AWNyG2Zz2qszsbSMdSXy0+/aM9iS7O9iQWZ2ZoQCUeQYRALonrib7LDLaOdDmjHYvL2J2ESKjLUhHjBrCvuYaeS6tAvzcW6mP0iYWC0+7jngd93mLMYxJniqLGG65WpWOkEb7eO9n8TCi6jqilPojAP5L/+9FAP8QgLLWftUF7dtjGwnQXgjSEc9oh8l7SqN9UYy2n1N6PWyUjnTE0ueMNrHVh57RPhEYbTpmzsJ4RMBBJwCxs9dFxst353gKL+I2ropAe+DFkIfunKxun4b+kXYYQU4rVcCySidvoDHQZjZdpeuI36Y3VWCULKgEl45CIiQ1Y2KSDlpnNS+GrDSs4QAsejDXNdpTxZCpdKTCnPemKbsbAVgE2stK4xXTKeDyZXS4geHuWbLNOCrozrqGHGpdsvjLNYBuukkPd1qZueNvptFG5xntikabAxbSaBfuDWnRa/GsnkeJ0DaMdtPCwJUA+isa7cBoUw2JH4SLhjVwMiAu/SjkfNpiWNrEsYUXIPa98se8+152nF2vS0fYPMSvM80x2fjUm0GoFUL4nt4XihYZqjG6m4T5eTlivoPjTL6HZbb7mQy0LXgxJGJvAx6nfiF99+6O93Af28QUo/1BOLnIN1hrv8Ja+1YAD9Fs7fGJ0JrbmNhimttaZdIRl+JKH3Z6lroZY7QbyCjy1ugzvcYyT/VXpCOckeOTlDlypjTDaRxsuUQCcAzXGl0cAICC9TYYH66938kMT+FFABOMNhVDHrnrvLxVMtpTGm0O9GpuILuI7RhtZp9F0pG8YBLR0tCYMk3J2WqgAlgE6UiLxUVSDEn2fgW767fpTZ0l4x7xUhGvJB2pMdr9BWm0dQTApUbbvRoD5zShR6yPU2BUymXkJj2mY8WDORhhFqVh4dag6JVr8atA2x+z6jt0lZbZA8tciNIRroveVAzZm2ph5i6CL6hqVne5dCQvpJOkI91MTzLaWlnXEIWB+oEVIAbWtYlGW0FjALTT/MsyLy5PnCACwvhkBab/3hjt0D2ySafXmAnr+0q2xlbs/XicnLjXu3eTLPI+LiamgPY3AfgUgF9VSv2kUurNcJ0h97EhEkbbg9JJRlujkI4kbZIvWDpSMNo0kfljCUV7jJ0aONO5BaPd0UDJgHbhOqKHwpv3IuPm6RxP4iUAUdfKg2s6A9C+zXy02TFXXUekzmsPidHmzgyxS2WmB2TFkKLrCNMIA3A+2kW25mI12sbEhnk1vbKz96tN3jothsybYATpCJNRVFuwN9ZoM8ZOKWCG86IBFT8vgE+fi0Dbvy/oWIN3uGf8eqGjLOlau15FWVSDxUVk6BnQFny0lXfgMDU7PO+jDciZvXjMqgr0ONMcHDiaFkM6X/ZNjPasG8uGWhmIBhzQXmEG66uGuVwDYFmss7RfABEOwRmjiUbbS2G0yz6NMKEQOW6TabSza5R7h8/NGmd2ln2Ge0002gWjrUVGe9eRgP4eTp64yhlt5jpSqTMJ86y1EXTv48KiCrStte+w1n4bgDcC+FUAfwXAK5RSb1NKfe1F7eDjGKENrIkNWnJG20JF1xF6OJiWjAa4rm/bJVHqlFUy2u57w2ArWHWNrBlNf+hA5+qESUsYo0qfUQzYUpqykdXdNnG+NjhSblDKCYLg4kHaxCM3WEtAm7dgz0FaXownfdcuIk4eEz7aUjFkzmgzHeuUdCRkawRHHS4duQjXEZJfKYw4LyQdbF+mpCM6Tt6FQ5Bk75e37yZgNDPVJjG7CM6uA8AMy7LTaw60O2B9Urad31ZGAfh6isriIsmQNAXaapLRpjG5rtFOGe1igcj01wHcFt/DrnPDRiaJXrzG7o4IGu3AaFeKtSl63+SIFhVFllFbgSBBUhTeTC6z9tdQ18enIZGOlNc5arTd8Rz0a5zZVPCRgNuwcE73JdFo+3qWNkDbvereMdrS9/AxOfi754s7Dq73XtoXHhuLIX2r9Z+11v7nAF4N4HcA/NXme/YYR8JoE3jiQHu9Dl3VALmz1JpZcF2MRttLR8xQVKcX0hE6Jj/ZUiYqsBpk8s+Bds5oz9Rm6Yh6uEB7PWrMdTrhUARW0IOR2SUPtO9EZpBXuGvtgF6N0TZdZCVagk7FF39TLdir9n6x8EbShhZV/UIbZC4dqVkN7iJ4/YFSwBznhXQkZFG8k8Qme7+kqyC9z4oha9KRxBefgHZjRhtwDaiKxQXLKLhXhfVpxobywkCpTXWWueixLgpAxcY4Df3SjXFAe4QpMiQDY3erTW2gwCVPBYsf5DIT4DYphlTNGe1Er5wz2qPAaHOgHcYExmiTx7u/Nx1Yj38TFtcJ0I7gtp8pkXXdRSS2udWOy3EeMhOMtmJA+3TMgDZntGnhnHdXZRKVALQbSkd0p9F74j3/nsTej8awZdalil2vPdC++NjGdSSEtfaGtfYnrLVvbrVDnw0xMI12ABLndY12GLTPuEtHBGABaDd5kN1rnFTLRg3DqFza1Q/IYX+8Rrv4DBqcTuvsielNVTpCbFxnxiaLi21jNWgsjBuwBquTjEM4ZpUC7dVdpkuXCgMrumjd6dgNsymjzaQjeUtm2qY3VcYocR3ptBvUWbvFHIB1gqZZlI6sG97b/r6dq2Wp0aZiI426Rps3lyJGj98LnsVUhruOVBhtgwC0hxZymSyjMFdLLNcVWRox2r1yzzJn/phlWKfHUpfOQCfgXTpyQE/2frP6wm0XkUtHpO8ZhwjSakxzymgLDWuYdEQbLXaGTIpeW/orJw4oMruearTL5jnjYKEwJtWQYez2vtHhM3zUpCMRaPvvblD0OhCBpeu+7Ly2QMq42SEdkw8OFZaYJ4980tyoQnINA/fR9kC7QYaKF6jTIih3N0nGZMrK5UD75CRe3H1B5IXHPQHtfWwXiY/2lHSE0uszDzqZ9U4ohpybCwXac7PG2Zhq1gaapFTKhNEgFz/Dg07/56szAWgTW01FN4J0JNgIaitqoy8qEkY7m8gCQ0lA26dcl3dLXTpvJlAwnevsvGAoOq/tIoKPdseKLjNGm/uY1xhIV3gTr2Ep/0mPx0ktZI2wUqjuyy4id9SZYTUJOh0bZIJFYfgcqxMngxw8jUz+U5OOhPtFxxqHdQM7sBxIOEa7XDgD8b509RIauH07bJNKR2zZVZDp0gEC2plGm7dgb8lok7uJUbG9fZ5eZ+yuJCkAUkbbuVHUpSOzhS4YYoAtqHrGaDeRUfDsk79vl7lGOy4uZn3JaFsbgStFPteEsd+HzGhz6YjPZrboekqMto7dVct28KyIV9ArBwtTf8gHl9wPZ6csQ8WyvNUGVNyvu6V0hMbKzsRM8bk0Jvt9rjHaJyfAK17hft4z2hcee6DdIIZRocN6I6MdwNXcYI0On/iDs/B8BO3o7GKANrHVV2enuDVcSrex5WALRNYisrvuNbgdnHLWLwNgBNKyARvgjLYtdJ8XGavRYNH5oqAsNRulI7EICACWx4KMgjPaudaPFeMBbsJqqlfuplxH/DYTjPYIHeQyIStxLrD4lLnoIDas0RgSoN3WdYQx2nlhIHPDIZYslx0kkgKhfXfwDufSkZpEiDHaTSbmTKPt/KBLjTadfwDRhYNNzrwAtDdDeQ2DRtt/jxa+J0hHNhdD3rnjgP+P//g9HW7ymU6j7aUPeYMRYma7rs5oZ5mLKenI/EBhQFeMyQN3HSG9eMtiyD46uhDjHrYZ4zEHRjuTUeRAO2e0A7j1sZhbnOIgXVzbVDoCtAHaznVkWqM92kj4yBrtjNG+7K7x6YuR5OLzUK0+ynWGdJ8b7DqbAG3rpDtaV4E2L1CflI4884z7ec9oX3jsgXaDWA86MAkBSPBUWu46MutwjCO8+k+/Ht/zPWETNxl2pmlVc96C/fr8BDeHy4kDkMRqANsw2nXbNzMrpSN5hfu8G4sulRcV4+iKHefGMzsZ0I6MtnsNzUHu1nXpQd8rVsGTLr3svLar4wHgOhNWdNFBrzyhqR1ZAagoHcmkC7OZEqVIlHZtamnI5BoAMNdLgd1F2I+g+5SOmbG7JaMdwW21eQu7/y8EaJN2Wg/CvqjkeQ7Fgfw6Zox2rUlP0lyqptGeKK6l+MM/dK8/9mPbHWfyPX5sNd0Eo8012n0pKQDomKO/8spmtSqM0V4cuuM5z+yiRyYd6WfAgK6NXpkV4ErF6UCm0e5t4ToSgDiTjhSMNiv6A4Arl0bcweWUIOF6ZQ86WzPaNY0279YrdXrN56GDK25lcfp8ZHlFjXbWgj055oV/nk8bLKh4EW9FopIw2kYX5AeAPaP9kGMPtBvEYBWzIfL/JwDt4Myw6LDybU7e8Q63yTrTFAJtU1OUXr82P8XK9kmRcjExkyd0DrRzRvus1LEGxsiz+KnWL9VoL/oBZ0P/QMd3v0HgZNEx6YjEaHvAeHDgfj87FjxbSQurx0I/x1PAAJx3eAs3CjZ5VBltrtH28gbOkgWnlbBYKgf1XLrQzxWWOWBhQLtpZ8hML+4Y7QnpiLc0LAALL4aUWDLGaCsFGCWAW+Zu0i28PWaLBVXW9KnvbAF6hrF8nmWg7d83Y9lhTwL0uTPJOoKvYAlaYbRvuQasuHJl+2MN35MAbZld5KCzxmgP3DnJjBNyGY3FQQVok+tIr4vCwl1GGE8nFjGBATZGbJ4TzokgHYka2oMY9wAAIABJREFU7fisAsDlS7YE2qPAaDeQRaUa7borkpnQaOf37cFVVwh5+kJkeWkeUsxFKL+GiUTF29ny+pxdxciOmawGJUZbkesIkR+c0bbWXa9XvtL9Tg/bPi4s9kC7QQyjQkdtYCXpyHrt0j2kRZ5HIEIr0+GigbZHEtcvu4H4xo24TT4xB09ovz9cfwqw9CMbEHiHK8Cx+IV0xLP49EHz2YizIdWLX1TQfDTvZUY717YT0D49LuUyQdNMbOi50MintXSEAbCqvR/LKOiFO+9J988gl5mQjmQM/WzutaxJg4s4eQenlYaMdsiQ6BXO1xno5/Z+5B1+llnd5c2lcpYsWyxJLHLSdp402hfAaM96YHmeafFHJODJEPDk0pHEgUMopKPCwCBRGQrpCEndeYakVjBG8vD7AtpbSEc46DSmYgEIDWOYjWOlMZHpFObeqOIsbaiZFEMG0HnagtFONdqAsEDkLdiJ0U7YXUDBisWQdG/m0pErl4HbuJJJR3Ah0pGE0Z6SjhAAFtxl8vHp4JoH2hXpCM114mKViiGP3GfkXvS7CF7vtZV0pBekI6uVe3affdb9/tJLO9/PfUzHHmg3CCcdSRnchMkJ0hE/eR/IQJt03lEnhpKGfMDIGe3rT7svu3kjTlTBRcVHTaNN/x+kI+cl6Ax65bnBCj3sCZOOsNU7ACxmFmeYN9E4bgr6ylnnmZ0NxZABaJ/E85azuwGkcWCa6dKNbiQd8Qu9yYY1rPBG9ErPFhdmZqrSkdAAaeFbOrNC38GmLJlWo7s/dtyxjLtEAK4wcJk3IWH3bmBdz9L7jVf1S81OeDdAwLt0FOxi/B5itJv6aLOMQlE8lYGnAEhyRpsyVN1YsPg0niWLi7zRFS1i+mhp2JTRZl03JUY7EBe1pjZsTHaM9oR0xDW/xdl5ViTHLA27hh0DeSasq+jfE5DWl4sly1h+ioLRHuMYBwCXryjHaLOUZ8JoV1jXXUSi0Z7VGe0gHaH7elXK+YK933V3IU9fjvMQb+SjFNCpdcloW8RiyIZAO7i+aHZvbyMd4UCbyKwnnwQODnD3U3vpyEXHHmg3iMGqUCQnMdp2PbgUfCiGjPIIemDWa1XovHPf6V0Eb9kMANeecYPGjecYMMoZMALaq8h6uM9wr0E6wp71WAzmXt/4xxTOcIDf/P+usW0iYwG4wpszLEra6AKCxua+s9BqnCiGzIA2uzyFLt2UzGHhOtJKo02gs2d1A/kkNdjY4liQPOVafD2rF0MGjTa5M3BHnUGHjA+A2NZ5x+4MQ1b4NNcrnFe6niolA+3YmMj9HizbpMUFgU4zlr67vBjyIlqw077MNFardBHjMlTx2odW4XkxJCsALd7P7f3MWO2GaXoNc+AzJBXbN2K0r169t+MFMkZ7XgEjXOsqMJ25LKo3tgDa4Zj7CLRL6Qg9Gx36lnpl7qNdkahwoD3rITDaJdDOGe1cNnj5qZkD2i++GD/HlkC7VfY1arQngDbLuOXyxEh+uN8PnnAD99mNiaJ8NRQLB64F7y+1ZbRD06G5Z7Qzvbhl2SdROuIXRXZxgD83/lNcfuvf4gZDafzSL5WFlPt44NgD7QbhpCN1RptcChSBq0UJtLl0RCnfjhxdAlh2EbmO9fqzhwCAGx+NT2JNOlJotIk5nGK0PWL5lm/VOMQxfubXvzhukzPaBLR3vLjYJoK9Yu/aMufdtqqM9lkEG3RuC9mBJB3xrJTWUX+5y9iG0ea+uxFo1+UyoaBVmsg8AJsdGDfBMwZsZQ16nQJt0fv1AYOn+gFg3g04X5We3uTAEVj8DGgDrLaAPO8TyVMmHSF9byKXiZ+T+9DvMop9WRgHGFkBFHdMcMckLB5Y59qu8zZ1/JgzQN/1KFj88AxxjfaZnJ16IEabgHbPpSMlix+YQYHRDteZsk90TgQ2VBsmHVnKunRyHQHaSoTMjGWfisJApu/tJ1xHBEabNuMSCQC48tQct3EF9tOfid/D7C+ps+Sqwb0djsfEGpJ84eYAsPvZSM5WIbPq9vPgSTffnd5IfcGBNFuzFvy6g3TkwM3fTYohmY6+91mhIkPFm4j1daD94buvwC+ffxUA4OWXhS/71/8a+PqvB97ylp0fx+d67IF2g1iPuij2koA2gc7ZUQTaAZiPXjqiY1HTGl3aStXHO98JfPd339++xoHH/X7tNZcBADefi8UhJQPmXok1yuUnkvNCLh25/PQCfxQfwMdfPmLfkwHtBR4a0A6Mdg8YPaHRJmbEA+2Ts/hIFY4ikrtD4TpiCwvAXUQA2ozRlqQjofNaANpMLpA5rZhZh7Gm0dYRaI8wGG7HBeJy6DDT8SC1RiFB2UXk0pH5XOE8Z5qZjCI4GZwuk/dpHwE3kRVFvLRYJekIXWeeXuc2glTj0MSlIAXaYaHDGEjHUm5itFXJ4nPAwgoDAWA+R9kMiGu06X6qAG2a+Odz8e3JiGQBT69n2xBgAXyDsC7xnS6ycjNB8kQZkt7UGW0GgFvKKLbq4soIksBoFw1rrMhoc3s/kkgATjoyoMPZJ6LOlzc36r2MYnWnBbvLTAZILpPdT6NVsYZErANKs1wHTzsr29NbpZwvECR6lF1HyJWH5sOT3TPBSSam4lZkbcpo16Qjzy9jukhMEtOBv/Od1f153/sc1hh3f0t/VsceaDeIweogHQmgZiloOv0kdekJQTpSWHDVgfbXfi3w9rff383PJykAuP56J+W48al0hc8ZsLp0JDbC6PSA5UoFqigHlNDaaWY5GM+B9oHCOeYPl9HuEFv5iq4jdUa70GhTyppLRwi8epDWqkkPZ7SrLdjp/CslAqNcLhOKB0/r3TD7QzcLLW/Fa7gaU0Y7ZAwaMdqBXV9od0+yGAYkdmCAfMyR0a6zZORFHxweOEjz1nfNGW3h/K/QAy+8EPdlVNBcCkYMb8JoMxkFvc+tOFfpGHZ0MOJ4PU8lKlw6sgFoU/H1/ZSgREZ7QsfKAGPQNEsLKr+f/VwV15A3o6kx2klXQdqXFq4jXDoypdFmHRvzdvDjhEabzl+e/aCMw51PMjs8Bm67y24gXN3e/ZiduI7QojgvAIVKrAaLhlqUcfCXbXHd7e/prfSZVxhjd9VuwHmeuWBkWiCWTlow2iwrcdkvYo7zYm3esKbOaL+0jOkicUqle+MjH6nuzzvf6bAGN0vYx+bYA+0GsR51kI5IjHbOtF26GrWA0Xpte6BNcT9S5nxivvoFTwIAbnyGg8GUAdskHQGAmRmwYqnXXK8MADMC47QvTIMHAPOFaqrRvnUL+JIvAX73d8v3Uka7BNq500oA2mxAzrMFIqNN3tXkR2zKNu27CK7RjtKRiqYTrLZgOV0MCWTANHcdOXSLSA60lzZjtJVtw2jnriOHunQdGVEWLgu6dBWOWepomi48Qpq+wmgHe8wK6HyQyBcX/eHM7QtntDM5QJBS5Iw2LS5mqmToM4320RFwbA9FX3wzk6VIPIjRvi+gHYohVQEU477ExUWwVzw+Y++711AMOffXmTX34DaOoRhyQjrSL+RU/y5CZLTzAly2uAi1EokVpw0NbSjybGR+r1x2CU/c/gz30Y666P6yOzGrO7sfswOjXSEC8n0x867MxOT2fofu9fROCrR58f/hbI3TVabX57p0ygI0cJcJiwulYrYgA9rc3i8UqAuM9otnsRGdOKXSvTHxENJ98RDKph7r2APtBjGMrHseAZZVyWjTivnS5Qg2E9cRrqMkC64Jjfb9EL+5S0T3yqdwGbdx8yUmGcg1nTnrkYFOwOtUma6zYLThmkIsWbq5YLQPdVPpyLvfDfze7wHf//3le1FfqqJ0ZF0CMMPSh70ZcDrEySwwnTpjtBOphXuNsgNb2KTtImTpSIXRhlxbUABtD7K4HR6lZkMxpGe0OcOVM9qazu+uGe0sWzM/7HBu+xRQsmyNVLgcwGIAnbqYvHlnSIA9qwyYihrtJv7KqQa1P+zdc8hEmc7nnwPt0g/dda6N72/SaB9d0riLSwnVReu4blbpJ8DigRhtasHOO/mJjHamqb0rLJaCK1InSEciuA3SkUwuE3oBzLqmjHbSgr3KaMfFRX/g74O7qV90tRiSZIEWItC+80IGtING2y8uGnhKc/erSaBN0rZ52RQtl1YFguROOs4lQHs+4MQu0kwk+x6qSVq2sHFk8p9QaJoVXXJ7vyAd4aQFMdqnh+G/zk6ExR//m8pYTP+9Y07ksz72QLtBDFah80AidAwcYyFKmJhJr3w5/m2QjtSaSjRitCntjdkMR+okWeHnriObNNqAS58vMSuBdhdvuZkZsWLd5Lh9E+CA9hJzjMdtgDada8lVLjLaalI6wq2vDvq1a0/sC8+CdMSPgkbQwubdMHtjXYe9XVvdMR3xRukIeNaizmiHz+HFg7mP8yX3ACyZZnM59piZeG8fzAa3oNrx6F24jlzqnBSJ+cgOzJpMmrwj0I6ygyIdnYG0vlcCo43wHUGjXQGdDxI5AJ4dlExmsXCeZfeltan9qLC4yHXpl65qHOMIuHkzfg+z94vndtp15IGkIzMzCbRD3QzZKzLAEhh6ko4szH1JR4Y1nHOPYYx2C9cR5r9f7eLKjnl21GONHvYOA9pM/0shMdp8jCPpyO2X12FASKwgCag3c+DIxqe8GNKmHZdrz2qw95P6H+RAezHiBIdJQTEvhqR7YXna4DrzBWJFouJ8/t3PonTEP/sv3Y0FEGcvCYQd/xsmNeMRGO1P3xTf34cce6DdINbWhIn50C8iT3AYJt48fXUpZnRS6UgGbjcB7fshfnPQD7huiGd3U0abD7bBVcSPcdGLmElHOs9o+0Eu9xoGnLxkuU4Z7cTe78hte367zfKZzrWEaRPHBFMWQ0os/sFsTIB2wZJJjDZre+62sYXbwS6CM9qxBXuWah4koC0UQzK7QqAiHSGgR0D7dpzsckb7cD46kNaI0aZzO780c0A7LwzMZV6CdITeMzOho2mWFQp6WIHRVqoxo8262gFA7/3q0w6suXREp5KmcUw12vMtNNpXOncNGaMtS0dkoE2X/oE12lOMdmiZ7RltAWjrLYA2L4Y8y1xsQpOYrkN36O79pkWvvam2I08ayXgJ1+pWKmeqFUPWXEcCoz0chkVVIh0hGUUrRnviWaV9CYvihVQMmY7Jsf/BFNC2bv7m2QB2zIHRPmtQW8N19nRuM6DN7UdNJzSs8Q/VS7djLdjpSwKO4ERHpU07fezZl3/1PRzFPvZAe8dhLTBaHYrkjrypxjGOwqCd6yg50OaMdiIdIR3llHTkve+/5/2VtNPzbkhSS7nrSPDJ9mlTSaPdd0gZ7YxpA4DZUYflyToMBIV05Mi9nt1u4+sZJBRCFi3RaAtAO5eOAMDBYhpoB0Zbko6Q7MB4kNbKgSNhtLNtWJo4Au10AuLvSVKLgt09Khntle0SRvvwwDNGrYohiV2/PHf3ZMZoT7Fk+eKim09YhgWgXT6r1PWUA+2hSZGcYO+XAe28YVBgrBnaTaQjc1NltING+/oMa/RYPs8Y7SH+vbRw4xEY1AeRjnCgXTSsKaUjw4kAtInFX3TFNeSLC2IxC+kI0z0HvXKL1txco11jd4dIkIQFL6uVsHaiYY33RM8XZaEYEpfjfGZ1wWjnOuJdxGiZQ1BNOgKdaLRHmKQpWshK+L/XGpjpFU4Z7hxHvwDxcXiIAmgPbI6PQLuBFp/dt2ERxIp4iSQihl6U4flV00u3+2DScHZTSH/zOaditE3P6Tnuwx7oczj2QHvHQWCk8zd0ArQJgHnQo/zTzqUjaTGkULA0xWh/23997/srAODFbMTZKXMPyAbbwGj7hy4v+nPb2ETXOWQAAABmb3i9K4b8xV902wwZ0L7kRv2zuw387jAtHSFGp+ca7U2M9sI6oO0HqVyLHzyYE+lIWkgXMhc7rjYJ91zHNNqjTQ5elo7UNdrhXuXuDXlnSF8pv7wbt1naDr0WUrOtiiH9uZ1fnReMdiIp2IrR7urFkMyZpGS0mRtCBQzuIqKPebT3K4B29jyHAk9WDMUZ7W5mSilMdm6PnnDX+e6nIxhZD3GB2ZTRloB2xiInjDbZpImMdubWUpGOxGLI3MUmgtfwPU1s3xijXdUrx3ubFry8VkKSjkiMNs9mJhlaqkWRGO0GxyzKvAR/62g/6iU1bEElzUMHZpkMt7kRweFRCbQlRvu8FaONdNxYMYlKANrRzKvKaL94s8OzT7lzIZFXv/WBy3gXvtp9WwVoB0Ybi/s7oM/R2APtHQcNUHnHQP6g5rZvnNGmiaZsKrGZ0b6fmz9n/QBgMU9bC+esRmC0vexD+oy+gywdYQNc/+pXOobxwx/235MO/ItL7otaMdp0raY02l2vIqM9UQwJuGudaLTHdAEyxWiTvKHvbVOgrWddZKKzAVkE2kz3mevSRWCaWd1RMSQH2jmjfXRom0pHgkb76gFGGKyfZ4WBGybvktEW2N1cOjIXNNqjLZjzJo1M8oY1c6/RToC2zhhtky4AA6NNhYETx0zSkafcQHf8AltcSLr0yjE/EKMtSUfOcklBed9yAFxkPw57LDHLNM1CMeRpxpwPCNKRluxuUgxZA9rs3p4duI24hGvK3o8Y7Tz7EaQWOMiAtvv/MDectCoMvAeNNm0jAG3KxAC+tuacSxhtcsyHR1pktHXOaJ/vHmhzuYzkbpJ7fhtTB9ov3TJ49hXub89ulaTG1/8fb8Z/infhb+L78eu/1RXvA3tG+35jD7R3HHnxlNbAwXxwQMI/qPmgzhltmg+dRSAvWPKsE7VQy7YH/OB3jyEC7QOFs5UOwHIYY0t5gDHavmgvBzSA06luLIZceObPDwpBo+1H+/m1sj3uLoPGIkk6kjDaE9KRhNE+VKJ0RPmNRHcHGig7YrRVwUDuIuxKkI5k7Dpn/US3nCARQroNk47ExUXWJfSYM9o9ei4dodTsjt1lcvZqftntzPktmd2V7P2KYshFvcAqAm1Tuo4wezmlnNPJaml3X/SaA+0eonSEs5Rdzmiv10kxZL8ogTZv3gIAl56eBtpBLnNR0pFMluMKrbOMAmO98zHsypM9LDTuvsyyT+yYQzHkaXr9eLfFwA5fFKM9pdEmkHaH37eo2vvR+DdmQJsWGKc4iCQKc724ft29vnx390BM1Gjnx8wzMXTPcaA9pOMbAMz70fVzsOSiFd1NAODwspaLIf2YEIohG/BBvAA03E9ndcJHlI74h+qTz3d4/evcH5zeKRdCN0/d+PgD+Bv48v/pPxH3Z89o31/sgfaOI+gSTXxQjw5GGWj7yXDB7lmaDwuv205h3R0UQJv/evrM6+95f0WgTbZ6Xsua70tgLTBzk7LwGTMqCAtAO06GYZvMESFnWBbX3Ik5v1GXyzxIEK7YyGh3JdAWpSNH6YA8ZoO6EfyKh8E3RzAExkuv7V3EvTLadFz/98mfDecnHLPfV8l1pGAGie1hDNfKdujZ83F4qZzIdhE81Q/EDqxcLz5ahEXklHd4vIaCA0dukTkvuwrmNmnzbsB55gayi5CB9gz2tC4d6fKGNbl0RADa+bk9uuJO0PFLkjc1z5A0ANqS60hRJBfPfwBgpwKI9vft9Ve4e+XmyyyjQ8+z/x6jBpxla8OBgdeY6m/A7nLXkQqjzbM14Tm8kxZiVztDetlPLh0hRpu7BHFG++mnAa1GfOaEsUc7CrmGpJSO5Na6SeaCFn+M0Z71Fksbn9dhSJ/Vw6u9G5/YZCv5aC93X/PqF0vpfctdbCSgLTHad3GEl28avOEN7r/O7pQ7e/1gM7kTXEewuL+H9XM09kB7xxGlI/H/jg5tCsAy7a5iMr9zn34a8mLIDlj3i8Q+C0h/zU31twkRaF8y7kF6/nm3L7ai0fZsdJykmCxkpjLpiMBoU1rbDwqBSfAnZHHgXsXCjR3EFNAm14JNjLYxbBK6ZBKNdj551xhtansOsC58raQj3Ec7G5CHUReM9i+uvw6//dt+++xeEaUjNaB9HM/d0vaYcaB9uWsEtFN/6/lh6WJzz9IRWnSdpSl4vs38wLjUasFox3tl0XtLwwkp2P1E4friz38CKq2WHY1q0hHSaEuLCwLaVItyI36P6267dgy+UFzLY+eMdmapN0oaeQ7AMnu/60+64+Id8HJd+sKscZ6xmE4iNKSM9ukFMdqSA0cuO2CFmVaQjoQiT9/YycmM4n1rDNB3YyIdGWBiAaIBnjo4xqfPr+/oSGMMow5ZifoxC9IRbte5Kue7GWVfPZDmTawA4PDaDCc4hL0ZgTaXqGjtOyGP3e7lb+y+DdeGacElJ6gCaK/X+BheCwD4oj/aQ2PA2XH5oG3Tv4HkMeeY73zs+myOhwa0lVJGKfU7Sql/4n9/vVLqN5VSH1JK/UOl1Mz//9z//iH//uc/rH3eJvJUMwAcHqqE0c4bXPBYrRSGwftoswGu74GlOSyANme078tHW2gks7jcp0Bb2BcAASTnkxTAQHTGaHN7v36mMkY7HeBCwZGgJ9tFMDezIqibWzdz7X7rPtrxbw6vdKl0JGP9TG6jhrIANHga7xpoU2p81sWmSIJ0JK/qBwBl3XEQWCc2SGSAiy6J7v9TRrtH37GJjKzhdg60CXR6jTZNVHfSxcUUS8blD/Q6wqRt5/02dF4XBwrnalEUQyaMdm+bTFYSow2k5z/XaBcdS3OgLViLxqycL4b0QPvuzVR2Q/cTMaHHw1xE0w8EtEl3O++q0hFJFpVIR7IF4rVr7v9v3GQ+/+v03p53a5ydp2O489H2xZBC8dquIizuuEZbbFjjfg4L3rtCBos97KHYce3+wDHE6QC5mDGgPY5YI81QvfLyMT4zPJnUtOwiHKOduY7k3uFQCegHcumIe00Y7Qxou3uFA+05RhgsX5KlI4Czs80X17uI0cZiyNh9OF6vPOOmtbP7s2dszhwGfBSvAwC87o/MscCZCLTXrKcFIE9BKy9bOcMi0azvYzoeJqP9FgAfYL//bwB+yFr7RQBuAPgu///fBeCG//8f8ts9shGlI/EhPLqcAu0AegSgDbjxK58ML10C7qgrhXSEY5PT83u/nKLryFHnHiT/4ZzpBDJG+/y8sCsEWEFYzmhzH+2ZT2ufk0bbJgNcANqNiiEnNdp+ou5nKureNklHDjVOVWRmc6/hbia5jqSMdnCsaMhoK+UmkkI6IhSMAZENze8VsWU5ZWs8AItd0+LAvkTKaB9d64tio11EDgYD0L7LrqONi0jRaUXwQgfSyTs2JnK/LxbAmTrIiiGRMtrzsRGjnS5oxQIqi2ThXDDaQaON8BkbNdq+oPv4Fgf0EWjPZsBTRyf4JD5P1OI3t/dj97ZUMJlrtElrfPMWKwrP5G9KK/yd8b/D+35LWKxq3bQ1N+8oW2d3BY32cSqjyIF2AHODe3B5B8SwDbkrnZ8Dw4A1umS+e+bqOT6NVxZz1YOGlHHLFxeOXc8yboIrEjmSAIwUIkY762FxeNlte/ISu/9trGEAHNBeYrbz55nXdkTvdh0mrXweCtlKRn4kQPsLDBY4w+lJmcZdjSb5nWdzKJYn7gv3QPve4qEAbaXUqwH8ZwB+yv+uAHw1gJ/zm/wMgL/gf/5G/zv8+2/22z+SkbuOAMDR5bRqOXZjlA/DAe3YXRIArl4FbtnLBaPNF9CnQ3/PLEK1GBKLAPYGqxNWwxhAKRsZ7fAZTDoy1xBbsHPpiAdhNOHxgiWADSyCnmwXManR9qnnbm5iEwDJdYSNTcF1JLP3C4y2UVir1CN7oA6gKloANtFoB0bb7XBYPGQyFjr/c1bLRCn2HIxIE3yerQmLstMJjfaRwgmOYG+30WgH6QgB7WN+HVWh0U4YbWJLTQrS+OQdnnn/94uFn4g4o53Jr+azVoy2e80ZbX7+8+e5aKRUY7SnNNokHbkTP3c9pAXdz147wSfwbAG0rd0R0E402pulI2JtQZcC7Rt3ohwvz9a8dOLo37/5N+IAEupM2Pc0acHO9qVaACo5YxynWTlJo63ViJPRDb7DqAtG+2Bh4/wwDFihT4H2MxafwTPAJz+5m4Ol/bUx41lltK2OhctC0WtOfgDAbKEy6UgGtInlf5kXFOtkjp/3bYA2l/9oDczMOhlbtultQEC76yxe9SrgQJ8XRbxAKR15+eViE6z8PbaXjtxbPCxG+4cB/A9A0Ak8CeCmtZZmg+cAPOt/fhbAxwHAv3/Lb/9IRkw1M+nIkcaxuhSYzqj/jaf/1U/Hh/jsDFjbVBt39Spwe7g0DbRxcM+pq7wlLeCKIc8xjxo8m0pHANb5MQHabIW/yKUjMqMNRLaTpwaBCIxa+WhParQZoy1JR0RGO7P3KxjtDhhUZu9Hmk4f/Vy3kY4EfambfbSyQjFkZE+OjoAf+pZfBxA1poEVz/TXfE2QO31I1ldLzDDryols11r8IW9HLoAN7sAhMtoEKDPdJ9f3UsqVQG0A2rwYckTh3tBUo527vuRAO9NojzBxcs4b1nTAgC5t/JE9zwFo32X6UcZoA8CzT5ziOby6GKPWKfl2zzGsBaCdATB+nUXXkVA34y50ANq343g1rMoxDADe+FpZix8WOU1asLvXpOtm0YI9LiLpeF64e8DeLxltpYDDfsUYbV2M/YsFBEY7vv/K18zwabwS9qMf28WhhtjKRxuqcEVKNNq0cOZF+Qvj5iqS/DHCAWBA+wYjSKxJFhez3hNPu5aOZPuy6Ad37j1ptxWjvV7jJq7h+jULY4CFWeEs43GGwUlOeIiMtm/Ks2e07y0uHGgrpb4BwPPW2vfu+HO/Ryn1HqXUe1544YVdfvQ9RZCOcEb7CDhWl8ONSUC76yMw/eiv/Du8Hf8NgMho5x25bq0Pi3Tcya04iNzPxJ03uABcR8a8qjxnNXozFhrtxN4va/0sMdpBP+of3iK9Toz2sQC0/+7fBb7u6+7pWPMgjCl1Ow8abc9ob9UZ8gA4tYt6KHUcAAAgAElEQVTg8FAy2hBdR7iVVDdrJB2ha+RBglQ0w1PNAPAnvtAx8ysPTPOFwxNPuO1evjuLx7NKj1nqmrZCptH2E9nxzR23nadJdZYx2ieMsR51UrEfjoGsvvKCVoHRJgdEem+xAM5sVgw56iQFP5+rthrtXCN/mh1zJh0BGCAJriPpMfMGL7l0JGi0T+LznTfdevbppchor1Ly7Z6DAHDKaNdBZ1gsMQaYfqYx+coV5waUSkfi9wDAE5f8AvSML1bjwjlotLN92UVsWwxJ9/TrnHIAHz1+KrwvdYYEgINujVMsgHFMGrOE9w9KoM2f52e+8AhnOMCdf/epXRxqcjxFPcUqB9q6lI5IQDuRjqS1QkO2KA5A+yZbXMMkZNqsRyNGO5sTZ6nkLHe2kuR8lHUIRIBZ4/QshX70DP7A5b+Nt7/hBwHUGG0GtPeM9tbxMBjtLwfw55VSHwHwD+AkIz8C4JpSivJ0rwbwCf/zJwC8BgD8+1cBvIQsrLU/Ya19k7X2TU8//XTbI5gIUTpyBJyowwJo81W1fvI6DuAmoLMzxyR0DNxevQocr+YYbt1NRMUnL8Sb/RQH93zzixrtQ4NzRMCYT8wAMOtsZLQlED1TWGK+lXSEQFjuzBCa/UiH9L3fC/zKrzyQxIL+9OxDHy+E2qTxrDHaoo/2gWMGiT0cMpcCidF2Gu0UaLdltL10RNlSL55p8alV9frTrpNiKHr19+1Tfs5+8c48AtNsIpOAtmO0Bcbo1q6BdkWjzYA21ytH20MVPeRX6cQuFdKt1yWjvbQzjMeMAc4XkSTPuiCgzVnVIVs4F9KD9RojTJBRxG5/7P7PtOCJftQ/Jw6wMEb7VSOexzNYfialyh4caEuMtk27nkqMNtNo86Y3gHuur85OJ6UjH/8Hv4Y5znByO5WU0fdQf4Tb53EhysNa4Ed/NLio3lOE8Wejj7b7+ZlngLlZ4aMnT4XzEhht5qMNAAeztZM6rlYio31A9y4rhuSM9tVn3YHf/gOB8PrZnwXe9757P2BUNNqjSlIiPBMjAe2c/AAkoK1loH07l1+BfUYbOUU+Jh/Mx5TRpgVvPoYtJaDt3bz6Nc6WMtC+1J/hq177BwBkoJ24juwZ7a3jwoG2tfZ/tNa+2lr7+QC+DcC7rbXfDuBXAXyz3+w7APy8//kX/O/w77/b2h13edhh5C4FgGe0WTEkgZFuxiiuJ57AAg5cnX34k1hn2rgrV9zrbVxO2qOevOge7ANzfl9AO6T6E3s/N/CSlnXIKqwBoO9sGJzCw84HnhmwUoJ0ZNYl2wBxlcwrrIE4Ud057+sz8Mc/vvWx5hGA9gt3gOeeS95LGG2jMOg+7QwWPKXj34RCIl9oMmbsbmC0z1Kv4aQDKDU7adWOnBUyyj7a8fz3X/YmAMD67T/jjyfNXDzpBVwvjdciw5J9T2BU/QBtR4sBHXoGtIkN5RPZLmLMsjUio2116VLA9Mp54bIE0shzmDPaQKoFX1udNOmZH+gLsfcLIJmxqly7C7DFgz+moLPPGmrduc2Aa7agMsZZnJ1jHnLOedOtZ7/QnZhPvj+VvyVA+/a9T958ERmuT+6ow0BnbJ7DgNOqBGDXD89x4zT6BeeyqMNXXMKTeAnHd7hLTawzuXIF6PUaLy6vivq0X/s14C1vcZzBPR8zSf66unSEX2etgdc+cYyPDq8OblJSMSQAHM7Wbi4hoJ0z2kcqNqwRNNpHl935Of5YtoL44AeBb/924Ju+6d4PGBVGm9cWjGNSDClqtKWifNJo88ZpWgDarMnLkDPa5FyyY+lIntlezFPZRiA//KI4SEcyjfYKPXo/3x70A86WaX0YPYN9BzzxGjcgS9KRFW9YswfaW8ej5KP9VwF8n1LqQ3Aa7Lf7/387gCf9/38fgL/2kPZvq5BcRw4PgWN7WGi0u1mKxhdf85UAgLNPvlwwCVevutdbuJrIR0g39sTh2QMBbW53tDhy+0UWQHnxFOA0aYVGO5OFrFSUQIgDXMZ2SgOcViPu4lKyuEge8I985J6Ol0cA2lgU+pHAaM+dfdZg5mnDgiAdiX8TGHg/1ubH7IrKMj/irDlCvzCydOTXfs2dkPuUReWMtiQdyQv2uifdTbf+5PPpZ/jrHIA2ngy1A1VGewnA2gD4JI02L6TbReTHLLHrHEgkkzfdtxnTKTbgWaegNrC7zN1kNZokQ7U40m2kI/4rqAFSvpgFykKuHIyHxYUHLCQR4syrZAs678cEaOfSkSdf79iCm384wWj/xm/dc7dM0d4vL95kEojY+IOdkwywAMC1S2vcxNVw4HmaHleu4AjHOP5n/yppukULZ6WApy+d4gU8JTqt0KN8P9jMSc5cEXW9MDAlSF73eSvnp+zHTKkYEnBAjIC2JB1ZHGhBOhK3IQeaux/PkNrb3nbvB8pCrKfgnvbD4ACwv75yC/aY/aAI9USB0U4zqzQ+3WW12oPVyWmbE1jfNaOdjckHBzaZ52l8Kpyg7rL7jRZDRAQcGddoiT1nlBToeoUrr38SGgNe/kyZYUw6Q+6lI1vHQwXa1tp/bq39Bv/zh621f8pa+0XW2m+x1p77/z/zv3+Rf//DD3OfN0XuQAA4xu50XGC8427MCLTT03/wZ/8MAOD07oBhLKUjAHAbV5KCyJM7AxY4xdHBWDgdbBNScYgMtDNGuy9dR0Rv0gnpSM52jtkApxRwabHGHWRuK+9/f/z5ox+9p+PlQYMGT8VRrDzQ7hauk+I6A9q1YkgAOD1z56HoTDhz1nZpI5NMo02Mdg60f/iH3bl897vv61jztLfkOjKMFUnBihj6dFA/PHTFOS/hyQiusvspWkE6lp60wr0kHbm7W6A9+GexYLR5w4f/n703jbrsOssDnzOfc8dvrFE1yJJKlmXLsi0wAWNsA8bRwstiEWw3rDRNWNB0WARswAyGhkDiDJAVsDthAY07nUDM0A3IQNsmeIXBbRpLHuRJtgaXqlRzffO999x77pn6x7vns8/9qsolom5q//mGe+4Z99n72c/7vM+7D9A2PeLNqAVAnvdAk9GeTSplGw+Bcm+jjve3Kh1Rk1HNsLc4X9blqqIlcrGjJgY2o1jCSYUXbKocLVoTrBLQzi/rMWkNaMMDnnrqmq5VfKcNaGv2itKmTvoRu1LyNG8y2gmvkHv5snYccc0caFcx8PTTynHkvV7rZbiK9cZzftvbJLHLo5XX09QqrouqJKog+cQLPLJ5Y2Nmm0a7ExX4LF6MT36ibvQVQGG01WRIRX0iEmP3jCjkeaYGvUG//H0Z7aJgTDM7T/VdFTkXFo02T4bUkv/luyoSYydSAmTKZQRYfw6kIxqjzWU7nNHmtQ1M1xE1MlQUKFTpyCDEtApFvwYURjsAnNuOIsFUk6WK7diYeks6cn3t+cRo//+itUlHADk5i9WjCbS79Pd0UpHriDJI8sF4F0MdaI8rdJDKBJWbwWjziTdl2mkr0Mb+jLZq71dZBjjBdrJBsNLt/QCgnzCgrTJCjJGpld+vu/3MzyD7/T+m67QAnpwBkyBy0esBY6dnZbRVRk8M7OxUTdATRUBW64OxmXjTptH+2Px+SlC6QW/aa2G0TWcMwfwxDbLJngDA6jC/NkabTUKcNbUB7ZtNkJjXLIC2CjqVJEVt8ubXw6+ZATAb0G5jtDUwXnvwFUvDuOs9J4x2rRQyUc9JZbRNprPRb9uSXkeB6PgNdhdUhEezBbWUegeA4so+QPs6+7ja59oYbZUNFUAQXbGNTToS8WfEAEkjL+PQIXQHrKop34+hxV9fmhPQNsDlL/2S/P3GgLZMuhT9tqg0lpKkI/I76ye72MKKwmg79mTIsMIzuB0v/4YVlLDY+yUOZgrQJrbUwmhP9f2Ke7CxoUcor7GpUhh+yo/hpU1Gm43J4jzqTpPw0aQjbrNwmvIMxUKzGGjVMD0b0L7Z0hEjn8LM7ZD91pCOjCeyw5YlckcmQyYrMe1DWdCqQBtHjyLGDNl2c2ya53SAW4z29bVbQPsmNwGivfaJrJXR7tGbO03rhlyjVToyrtFBik6PeXVfN9BmzK3igCJs9RSgbRYtEFnWLfZ+QUAVAKXrCP3fJh3hoLaq0RjU+52SgLY6gDGNYYAcD/2Hh3BD7ed+TpTitunNuHTEi3wsLwPb1ZJdOqIMthww8sJB5uQdRUBWmYy2DkZE6XqFab54EXjl+9+J78Gvf/lAe4FG23TGECWkC30f6qJsdblayGh7Hsl/ONDmjHYYKJMH1zRPb7J0pGzefwDIMkcyXIp2WgPavCoqf4a+AbSVdZ/N3g+A5lVbGMnNUeRQURvb+/qHfwj83u9d7+UCWJAMOdcXF6q+tMFot2nx6+WGR7zKaMdRrdmCmhptcS4Go61Wiy7h3ThZEPnifGzSEb640IC2YcWpjoNhN6B+y4E270/8nXccdO48ogF2c7G6vg4C2pcutZ7/DQHt0sJoN3Tp+oKqsxxhjgjF6WfFubqoZElT1pK4fVEGMHs/R2q0SToiPxfFi2YG0FbHWBYBaLSdHeANbwC+1AxcV1WT0f5l/BD+n4+xCOK8QA1ZtEjkFqDfyI+ySkfYvSsrV4sEDAaUf7CJVdoP04LrQPu5WTiXlQtflY50PS0CK99VQzoCTy5syhK5E8rxabXbDrRDBzh6FBEyZDuWwlJsrMu87i1G+zraLaB9k5tgtNUQJNfuMkmBkJcELUB7UqGoPM0ikA/Gb8QfY+NZ+QKkKdBBiu7AowH/Ol/0Im+eiwQKCxjtUKkM2eIoktXNgjWO3wTaskKjHnYFgH63ajDa1ZUNfAxfgRI+Hj73iuu6XrpoegBz0AnMkKAemdKRGj5yOAEB7Z2yd8PSEdWZoYKn2aTZWGST0ea4/KP46hsH2kb1Qte1VKlsk45UDll9GdcDAKurjp3RjpSkV6VqmmS05bmJ/jZ32pNeb6BVRvKaANqKpKmoPREa1yYpJqA1nVaEzGUmZQe80ENDOjJTgbbuuxvHLS4FVQV8y7cAb37zDTnPqG4UwCKNtvwO77f7Ae0trAgrAlECXJWOcKDdxmjz/rSxIBkS3pclf3McwPeqhRptYUWInnSCskhHol5I18P6gpAiKdcsEt0Fo61H5dYOh9jAWqN4C48SAA1C+ZqaWhjHqlcW1yy/I/rus0xzbiSfi+1igxU3pSMWez+bdGScBfoXRyPg2DH6vS0S+YEPAB/6EPDDP9z4qKybHtkAsHVW1ytzjbZgtCFrWFiBNpc5Khpttd86DrDSndM4l6YNiQrAwfrNB9qFQbjFXU8jhswcEumc5Mq5oiyRQwXavcWM9pEjiJBh9vSFRr7EnEXvZt71k3p/l9stoH2TmwDavk1SwELwwnXEANp9n20HFrJrJkMC+mI/nTKgvRRqA8o1n+8i6QgPJVtYDTE4tUhHOh0ClTxpjCaGUrOSMouZmEkoANDv1Q1G+3f/+hheiY9d13Vq7QtfAMCADmvzHaOIRl7BRwEEAZaWgHGRIN/RC5AA+oAvtZ/0z9Jg8UWkYKJ6GuuhZlEcZConTH6MOcJGwaJrbWKhw2USflsypH4uAAP+eW71S189QLrP3Qvc17UJWIRDzWQinnUYyONIABzp4HJ3F3j/+687Oc68Zu50I5hzSI/rEq6QdGiTlMFouyajXUkGrKhcuCi1EuwAMCt8MYPlla8nQ7YVrPm4Ul7gj//4+q+5RToyrzxpWWgsnIXUxVwgKiF432ORCwa0baAzimAAbT0ELxjtXf1dawDtL1P+5nu1HWizbqsx2oZNmmb7ljDtLr8emyywB4PR1her6ycS7GAZ+VndU1p1oFUZ/WttxGhbcgvUYljQmVkBtCc8J6ZBZgOQfRwAytpruo4kEPZ+dVmhNEqwC0Y7020DMRoBt99Ov9u84wD5zBT9sLgeZR5Sn0HwNI3n3DvaM9xy1AWV6YUONIG2bb5bHTCgPZkIiYoql4kiYO4+N4y2+g4lPU+TiIoiYqbriAm0FelI3HExdTok4WFNY7SHQ8TuHNnZS8Bf/7V2PrxM+8zt3GK0r6PdAto3uQnpiDLGCEkBZ7S5dMQYhwTQFtKRpk4MAMo9+TKnUxcdd4beckAD/nWaslqTIY3Qd1nrVkaAInFokY40BvWymXgjQEDOJnhlMuSt30eD0R5tyZl52bF4EO3XONDuy+INatUvgGyMAuSA74tkGK14xSKNNgPaZjKk1V6utLN+thLfOQIBAK+3VUYil0iGbDDaLUBbfc4Ko/1t/52PCziCd/3xfbQPDsCUbUSSXJoKP+fAbwHaqibjl38ZeNObgN/5nRu7ZiPSIhY6CsBVGW0BWJygldEWz1iZ7PJSD++K90dJTjYZ7ShiRW3MifnP/1z+/slP3sA108+GdESRI5VwrQtE7q1rLqgcB1gdFjqjXeiLE7omp8Fo+65FOmKYGfC/Q2RfHqPNzsXnNppqMqTC7vo+EAXl/kDb0O5ax/Yuk+wpJbG1ZMhjNBBuPK1HotbX5TY34uRZKlVc2xhtk40WNppsTK6Nc+UtkvwDKjiNZEgqyBSjnmWygq5CXgtGu0r0sp+jEXD8OP1u844D8J//ZIjHcJ+1fLsqYVT73eQzxDxJmZeu0R6h32C0TZvZOSLUGZOOWOahtWFOkYnJhCUX+gLQi3040XOk0VYWxQmTnJmMNjsXTshdxboE2kWBHKHot2KhpOjkBdCOXMBxEN1xjN5lY76Zl3TjM+UcbrX92y2gfZPbQkabaXdtAzYAJAMarabTZvJUEEjDiWxXkRVkHjr+HN2eg4nbv277N6HRjuTIZWo2TXYdUEz+5/OGvyygsEYpA9EWz9aGdMSm0R44DUbbH0tW92T9jES919rYzJYly+JfZ8/pr0KR14LRFlnnaShZwUXSEc5oG4O6vTKhodHmYEQpmc0HwRyBlem5llab0hHPIdZDcx1psX1jCyqT3QWAN7/VxVHnAq5u+9o1q4xRN6mFrIlb6/FnDxjAVGW0+cn+wi/c0DWb9ooa0OahfgUAC8DSGzQZbbYPDWizPpmXLgJXPi8r0IaHwJCOVPBQjAwd5NYW/tz7enx48NANRS9MjbZ0fQmUa9YBmGC0+QLRcJcBgNXlWmO0xXFURjvWn2FR2itQcqKBN96/Y8xujNE2mGafF9NSgIDpwNFNKqtGW5OOxObCQT8OQJ7RDemIcs39AY1/k3M6sIwUT3V1PLjWVlVoJkNagLZnY7TZUEre4s2xc17LiamsPat0BKAcHi658Q0pmOtU2n0BQM/j8GHqNC19+zt+/1txPx4jZxQDtLYx2umTVAOBM9r8XGwSobKF0Qakj3xZNRNARS7KZIK6KFEbi1UBtG+6vV8z+qRVhjSiyadO0XZP4i6N0S4cX8shmdUx6h25+NOANigR2ATjda0w2s6tZMjrabeA9k1uwkc7bALtdO4Ddd0KtOM+A9ozx6qL5kBhvqcA7bmHrj8nZwz0tHDQtbRi3hx4BFDImNTFYvFkY7TVSUrTskLRFFqANne1UC24eOsPvQbQ1isI1q3sSGvjGu3lAzgaEHB96nyibZLndYPR3oaSDLZIOlJHVF2PD+rsvgjpwqyS7g1GeN1WZIEPgnOEC5OqFjUzec3zGdBWEzPr/Rhtnd0V27m1nKQ46FH6U69bU9+cTERSnsqAtTLafBHw1FM3JB8xw8SOQ0ymCp4KyAUtP49R97DU5c7tGm2V0S6qfRhtUT2vyeLPxkaRnt1dvLb8M3zD3h/cGNBmp8F9tPl91qrJ1Z6d0S48XYuvMHYra44uHbEsNOPEbWq0XcsisoD2PAXQ9vKbwmgLMKLI6Mra08aWbqe2FhFrSAocmdxZWsbt7pByY+qUSUdqnSUWZMIlXSqRjWUkab55/XZ3VVXbPaXVhbMxbgt3n1Qdb5vvVZrLl5OiH03pCEART1HYS3mfHQfoRgW98/xZ5jk+kL0Wv/jp1wNLS9Yxu/GKG6QCJSlCu2YASJn/vtRoS7vNJK4MjTb7fqQz2gBEorZVOrJSC6AtJCqGne1zkQxZVK5GuAl9vIjE6OPTXXfRdk/glKHRDhrJ2vNdOdYKTBIzmV3H06w62W5QQ3EducVoX3O7BbRvchOJjgobJFmwGJjPUXAwbgBtNw4RYYbpzA5u+YDA3TIAYDIP0AkLSsqpEtRXrpfRXgC0OaNtkY6EkczUNivwAQqjPWVAexGjnXNAb9FoL7kN6UjKKrG94aUXrOGtfRt7SFkV4t7XHgQAPHWpp2/CgbbKaGNZDF4LGW2WKNTGaJsWaK4N3M5aGO3z578s0KklQ7qhAbTdfYB2M3IBkCY2z/TqeSoY7yk6VsFot2m0TQYMoMH+ehdTaLK7ABCHlWS0GQDmXfLQIeDECeDD+atl9TxjESkWzYrDT1557Yy2Emo2kyEBIBvrOgo1D2C/a/7u725qbMuCciH4/0+cAFZ6GX4P3ybBhsHGCUab9Vsb0B6u+uThz4B2bpSdB6japQa0F2n+FclS/vFP03mE9U1htAcDh85VdQmqoVVxbVTrtTHaXHMuXFR0HSwAdPo+KnjIRtyxwtG0+GKMu6IvmrJJgdfhwziJ08huCGi3uI6ojLZRvVBIRzj5Uds12hMlibGyRDNV5x3JaOs76sUF3V8+voxGeBAfwI9+6BuQLR20LiIbEhoDaFdwxPV0u8BP/RS7HrYwEAmtyrn0urXmOiIW3wuAtk06wpO+60kqAL06f4chMK+DGwfapp6KNSujXUeox4zRNqKMnQ5w7EiJL+LufYH2dEfecMFoJ2yusgBt9RQzm+ztVmttt4D2TW426YjGgs1mKNgk1cg2D0MkmGI6c9nErIevBCAZyUkqLUJ0ohLdLq02p1fsg/ZDD9X4/m98opGEIiYYZeARIICVaSXpiL6/IHKl64gFgAn2hFk82Rhtm0a7IR1Z9pEhRj6SXqncn3h5xcXncS/+8U8MNCngvo0D7dzB2hpwwL2KJ68uaZvkuUOhWd/HEvtIBdqLNNopOsBs1mC0NUDJk/GuVzoymbQnEi1oJtD2PKDyTU/v/YA295TWO27gVygYs2VLGOv1HcFu2RjtIAAcp25KR1TGxGL3te81c8Co6DGjUDkOT2ryZKLoG98I/JedB8R7tFA6ojDagaJFbjDa3JnhGhjtL5zvyz/2YbTf+176qa67ikJKCvj5fu+D5/F+vAm7F5mOuHbsjDZbgNgkQoOhiz0M5eJiEdBW7f1sjLZhX5n/9aMAgPjwMkpDW30trTQ8vQdLLgFtlY0zWPxuz9GAts1yVSTJKcmQHgoNnHaX6KLSvUJcs7q4EFHIVH/OWVqhjxE6SDG3+BXv16oSdkbbWDibFYoBNcrYxmir0hFXVAjlTQA1BWir/QAAunGpM9rKu/yJ4JXWRaT6utdAg0Axz+Unf5KdbxVRuXgL0ywivXyRyV4Nq/sVT8q3RJOX1zzMEWG6PRO6dPU4UcSAtqUC6L7tscfoJD70ocZH5rkkCUnO+HxoRtwA4NTdBqNdFBrQFu/7nrLY5eMyB9qJS1pwZbGqJu3O6vAWo30d7RbQvslNSEeCJgDjQJtPDCajLYB25ixktOeTJtAWmd5Pngf+2T9rnNfDDzv49392Cnj72/XzXcRos+SoEk2dXhgr0pEFjHY6Z9rdslkcQVxPwRkWp3Gc/jJ9f7TLJuzJBCkSRH6BzjLt4FcePqqZNYhW1+TiYGq4OdCeOwhD4K7wDJ7aXtE2KYsmo72DpQbQtkpH+HM2GG3N9YIXHGhLQMwkWOJAm4ftcPas5WIXtyajDVSezsAQAGvXaJvsiXrOxdxgtFWgPXDFpCtcRxSNNkk6qnZGG2j33b2Wa1aAdqxa0DEArL5nDzwATMsIF8cEeG1e6I5T6xpts7y6CrTHYzHRqS4Foi8Y+tzHLh8Uv9db18biq8RSWYJyC5R24jgdN93gTis606kx2rOZBJ3KGNbvAyOnLxcX7BA60DY1zQs0/wrQ5iA07vko3RsB2vST9+3+wMHIHRpAWx9Pu31P12i3SEeyWkmGLPUqfQDQHdD2vApiG6NtsrXZrEKEDJGbI9u5fhvHsg1oK+BUTfQFFKCd0RfqurYC7eNHlBwSNKOZqo0pB2jmXNZLDI22IuP5aPVV1kXkeCSPs4G1JqNtjE9xTO8iLxhk80IXOT5Co02LJXWi4f2S+/jbpCNiQbUzVxIQ5eeC0TaB9sWLwF/+5eI8ok99in5yil5prVVcGUi2LYqP3ObiCg7sy2jPdpXFLiN2ONCOYyBzEyuj7TklZtXNr4IpWl1TAvwNJv4/H9stoH2Tm5COKAO2yXRy7+oG0A4CBrS9hmUSoDCiij1cWsVI4lq3rPrpn249v/d+7pU6A8Yn1bjJaAsXAmOAo1N1FHu/BYx2TtXkhOuIo+vaACCvPWIXq2ZhnP6A9jnaYdc8mSBFB0lYIl6KxXaNcayqyLHigQeA3/99/TM2YownDno94Gi0iYuTobZJUaCZDGmTjgQ6Q++5lQAspkbYxmiTplMed5F0RLQbAdqGRtt1gdIzGG2DPdGKf6jJkAaj7QeO0PrbFiBdBWgLH+3A6Nth3bT3G49lds8NAG0zGRIA4ghNRlsBwA1PacNpxXGAJKqundEejRYz2qnecb+0I+2FTCectrb1lW8APkZ2l8Ro6/v0u3SwYk8F2vJzjdGezWRoPDSAtpIrYXqH0zUxRwRVOtIWIVEZ7TQX51E6/g3Y+0GTywwGwJ6rF5gq4erVCwcOJmqSHF9QKeN2FAElfJQzOr+yhFaAh58zAEzH9P/CKAZkJnzzNs+AyMkRdQPMRzcGtDno1+4rL3OOJkgWY/KcEFdlkeoBwHt+bhsP4BE6T4TNEuzKOyKkI4E+cHc7tcZojy/LZ/qp7B4r0J7syIHuLI43NdoGo+04QCcoJNCeN5nmXt/F2JGuI1XJKmpa5iGV0XYNoN0ZWoC2odEual524FEAACAASURBVB9VajzLt70N+LqvWzgnj65MUcADHn20IQssa3txKd7fbPlR/b6DkTPQCtaoXudifBoXshYAI++CDl1nFDFnEQVo8z7cD2bISv+5Y7T//M+Bt74V+ImfeG72/9+g3QLaN7ktdB1BAkynopJcA2g7DhLMMGGMgwluzTBkXZNWKk4cAbRvxzPYwjLa2nc/+j/h0UeV87UUGBEvYs7cM+BZ2fXc4RptBmgUGYVgtLmMotJD2ur1zJh23cYkiOpee+z/4zFSdNCJSsRDCbSLn/l5/QR/9Vfx8j/6Wbwd/6YJTIsCNYDR2MFgAKwlE2xkfWOTWkhH4picDMboNRntUAednaiUGm3jvmjXK6Qjeoa7KHs+azLaop05A2t79lngHe8APvKRxkdm+WjPY4y2EWpWIwpa8Q/lOZvJkEFAyaOAAm6VTXp9CbT5uN3v6GAwjmrNDQQADeQHDwJra8C5c/ZrXtBEpEXplyJRbjpVtNPQPgekFadNu5skTY12K6O9tyeOYyvSY1bD3Eg74nfVTtJsagXr7ccvCksiAoM6o+336GDFXgrUtVamGmjmDkhGW528gbTuoBwxoG2RjogiPNfJaOdsURnHTVu+a2nEUsr3ZTAA9hyd0S5M6UjXwdi1VAxU7f04EcAWAkXpNMYwUT2VJS8To70/0M5yB1EMhL0A2aS47rwL8ig3NNpepNnimeO2GJMLRn5Yks8BoLcS4lvxf9J5ItK07YDyjmSu6CumdETkZbBn+exp2SefzQ/apSNX5HP/ePIqi0a7mZjZiQphr2jTaPf7wNgdaq4jrrEQtUtH9HPrLNNLku7mVqAt3nlj4SwWPr/xGxIcGG3wju/Dd+C36A+jNH1R64XrzIUdPxeV5Or3gVHdkwvWskRe+w3pyFTRWfM+rgLtmWO3AOyHGbIyQD2dtl7Tl9Xe/W76eZ01QZ7P7RbQvsnNVozGlBS0JUMCQOLOMJ5RZ29NhszpA97xo8QV0hEA+ARerr0AJturgiBbyFRMvIUCtI3BNlBKsNvs/QR7gi4D2s0BzkwerOA0mAThhTpmgxpjtDtxhbgjjzf+Lx/VT/Bv/gafxMvxb/H25qBeFJghRlk66PeB9d4UW8VADNSAEoJno1MnYSFKU6NtsLtJWCqMNv1PdUMADI22sbgQE7dFOiJ20ga0f/InyQqPixeV1qwM2ZSO2CYYUfyjpTARQH29KB2qmLaPRvvSVfrg0IqOPCKeta8C7cmEOsDBgzfktmJz94lUMMgYbVtiIHfcsTFGSWzY+9UeAiWfgr+nAmgvYLSnSlEbALiqLPi2swRbjz2LyQWjGmhd48z//BtyOywLoFYoTCdvQY8Olu9NrdfsuuTGIogABp5MwALIuc9GFpgFa4pad0xoZbRZEloc3yCjbVyzABqmdMQgAiZOb6HriIhA8Qq5Rj6Fdk0MaBeVXuBFgLjS1cbkrPAQhUDUj0g6d51uUWXpCHZdAO3BkgB2dU1SM1uukCA/WpIhEUVCflRZSBbVxrSV0ebaaPaOnD1D9+T4oTnOpmt26ciGZIPfXX4/6ks2Rlv/Tieqmoy2kYg9cgeKRru5WDKBti2C2+nRPtNRKcZmqzzUlGjz67x8uVH8BZBj1O/iLXI745rVd5VXiN6b2Gs1ANT/54ikDXCbdESx7xOMdjcU22R1ZNVo9yP6RZ3HbmrjpIoSnfn/ersFtG9yE5OUwYy4bt1IhrQD7QyjjDp7q3SEAeCMlXiOup5gKwCqFKlOVqaHKx9QACWRSGG0XRcI3RwzlhRDzIh+LuE+Jdg1Rns6bRRyAAxt6Hyu2TfxZk7wmEwwRYJOXIvvAyysrTS1qE+jiE9RiO37fWDtq+4EAGz9h/erm9Bkwx5Sp+toQNsmSwCAJKob0hF+Ta3JkNeo0QYAHDnS7qXNkyQ/8pEmG2QrWGMD2kafswJtUzoSUvIudnbsQLsHTNBDlc5w6YoLDwVWBjrraoI0AASCej2yA7kBoG2zY4sTR5OOmOWjRVi89GnhYJHLdLpmMqTOaDsOcPhwjadwpw60bUWslP2grrExlxKmp3EHVu8/hlfdYRTv+KM/wtlflnKoLayICZ0WiDqQ8Pt0UcVoKstHm9ai3I1lNpNa11CfvAEZWcpLvRomwJ5hHd4Ao03nmyTNhD6xTU6Jqo880vioAbQHA2Cv7Go+wSQRkt/pdoFJvb9GG5BuFIUCbnkTESjOaBsgTUQhlaRKAMhKH1HsIOyzMu/XGbEpKunowp/Bz2y/DWe+xJhOEVmV3+F9m2unTW9x9aQDyEHHJFnUAkdCo20A7V7f0TTaly/Rdl95/xznxkuoZhll8yrXPd6kPvH93/QUPjc/hY/96Q7wrncJNFrZmOZEAdpF81x6PWBUmxrtFqDNgKTNO1wsUkalUhineU94or5o29skHQGs8jcVmNsTQHX5D68oejWlCdY2PjWiwEWhMdoa0GbzmWC0GdAW77LKaLNn3Y9p2wzRcyMf4auPG5ALPl/bLaB9k5ttVe04QBJWUqNdtriOgID2mGnozM81dqSukW3QJBENYo3RVn02AWD06Be1/Uy/5dvF71wvrgJtAIj9ArPCl8zIAkbbJinQGG0OtA1G23GAmGvs5nNitC2VIQFglLIT4NKRjl7BbIS+Fn7d2VKOZbp0GEB7/cGvAABc/dNPKpsw5oMz2h0HqduXjDZnEgzpSKwAbRN02ioTmtUw9wXaa2vt7NdoBHS7+Mv6VXjojSWKM+fF7GEmjHkeULmGdMTmLhMoyZAWuzwA8EOPtlkAtAFyZri84eEgLjf2ESewS0c40L6BQj02RjtOXHkcngzZZnWnJFhpORcdV68MaTDaAPDqVzv4C3wdgT0uUVHOg98T7i8OAEhTbGAVh/v07j6EhwEAn5rdo1/Yr/wKJTuxpuYOFKUDzwSDLP+iGElGW5XTAPoC0ebAwZk0HlkqSgeB01wszRGhnqoabfn5fkA7jtFq7/fUU1SR/ju+o/ERySigA+28DiSjV9eN50xAO1nIaJuJjDZGW0SguLzEqAAq9mEsIrMqQJS4iDjQvqiXaN+vlaV0aFJZ6c+dZUm8Fgmj5wGRLzXNdW2vDKky2gAaoFMtcCRcR0KD0e57GqPNXVledK+DvPRwGQfJn/LHf1x8Z7xNY9X/8PcvIwor/NbeNwPvfCd4pnsJt9FvO3EtpCNy7lX831eA7Wqga7SNZ9iQjljGQbEwnpSKdMQStZ4ZE9jWFvCSl+BxvBD12WdhNnW4ewp3NhltY+5dY8WMN2Zdkv/kTbwh3lUOtA3piA1oFywPwe9IoD2rAl2jzbzf+0khv/9cJETyCe/y5f2B/GTSao34fGq3gPZNbjKRSL+1SVzJ0GxLwRoASLw5xjkhMhu4BaS3a3aJmOpoGGmMdgNof0pfGU6v7GkAzEGlOTMADGiXvhU4AYzRrrl2t7mqDkNKDFQ12i6ag3onlJrmhRrtlN1PLh3posloK5P35o5ywjag7S6J/a8doH1vPCmZfyEd4Yx2B0gD6c0rtHHGfYuiWpRtvjZG226px9kDQB9H6tUFQHtvD7jnHnw//h0efuQIPnHyW4Af/EHaR6kXMHFdoHRtjLa+S5XRFuyJcc1B7NE229uiP6mTvwCVoxqXNjwcwqVGhzKr8AFAPRrr0pHr1LEWxuICYLZVC5IhtUloOrUuLpKOg4fxEP7wkyfoOLWHwLClfO1rgQs4iqfOxSJ0uy/Q3tvDBtZw50Fdp9mFMdlcuICNF32d+HM7Oaol6fotYDAfzVDnBckBDMAiNPIa0LZIRyZMtlbq1n2ARWphREhapSMsQVYAbQujzfuTqXWma9aZZnGu3KmIy2WU+9/vU2g83yOkI65ZqZAr2OiZlI6Y12wmL9P7LD8XIA6hQFVVRQuBKHEQ9mP67DqBdmGcy7cz7mS2OeGXDEAH2gDZ7okoo8UvGgAQBNfEaE9zX85lJqO95Gka7QkrKvPCF9MNO7v+AG2o2A+Nt2lnR04EeM1rXfzVXf+IPuDkhoXw6XSk64jUaMsHsLYGjKuudOmonHaNtmoz2+K0ko7rRql37XNmNQiAxpjpFL9z9XV4ER7Hn3y0mTuldvXP40V2RlvpT4LRxrrG4lsZbS63bNNoQyY75izvi8vM4pgld+5KnTS3FOx3qHM9Z4y2OuGdPr1421e+0urW8nxrt4D2TW7iZQ9MoK0wndUC6Yg3x2hOnd2UjjgOMRI8DJldoQEoWupoQDtFR0skGD2tv7wpOiIsUypJf2qLA7LwEYOKBfQXCFBludV1xHEoMVAw2qW93G8S6UC7VToyZaMEdx3puE2gray+N/eU7BybdMRTgDZnCc7KUa/gIUbBaAOppzDauR1oh6Ejno/p72vXaNvD60VWyoxwZdyZLR9ezGjffjvuBkUw/hKvBj74QXE96v4JaOt6WJsW3/fRkI40KkPGvpSOWJJeVaB9edMnoG08aOHBzMDI7i6wsvM0fvXJ1xGjnabXPaiXBeBDZzvijgLoLUxzw1PawnQ+8QT9/N4PfAsAWnCanvf33ks/n7kUy2RIxWnFBrTrnV1cxTruuk0Xer7E+Zy+yNjdxYazjsDJ4aHA1tLtQjpCjHYLGBwT0AZs4Km2aLQt0hEWWcort8Fom5aFJrvreWTH1rD3Y0xiHNP7YGPJ+L9Ua1PeitKBr7i+CB0rHw7yvCEd4d74POF0UWluLtErFmi0OStf1rq7iQa02SKSLxaijo9oGLcz2k89BXzv91o95EvDr/tnf5Z+TjLm4MRlFAbQ7kQK0G7x0YbjaN9rBdpF0Grv1x36mKKDckx9eTKm7V7x9+iG/MgLfh95f0VDmuNduoe9tRh33AE8c4kdaG9PRFZNRrvLJX1pak1c5sB0Y4fbzDZzGK5LOpJisUabvUMARG7Qf/rcywAAz5xtwi0VaD+Dkw1GuzCIgBXmQruBNWA8btVoA3JRXBUVKqX/NwpqQbH3Y0BbLJp3Z2LsmY/one13K/n95wpo3347/X7hQvt229vA5z5HPuTP83YLaN/kZgs1A5RMJ5MhFwHtHOOC3gSbtCT0KwEUsqs0k0TLHQwVd7oUHZ3R3tGBzxQJ8EUCY21AO/JJs1lOaHIwBziV7bFptAGgm+zPaAurtNnMOsCJQWPGzo9LR/r7AO2xZEqmGxPgd39Xar6Kgnx22f4FS7Dji32UpdNgtKdub1/piJpsVxmMqpXRNtgTyfp5QvugAu10uA/QXl1FFNC5/SVeDdx2G+2j0O3YBgPgzGQNP7rzTrF/K6OtAu026UjMpCO7u6wCqD2hdTxxcGkjwEFcbnRuoZ1mk9SFZ+bYwTK+74MPYbxE13C9Om3S2et9P46ZBd1UTQxsugfwCdP2nHmA5NWrn6XjVE3piHjW41zRaOvaUUAH2pPLY2SIcedxeiBf/dW0jVsXevLYzg426lWsH/axsuZh211TpCOuBjoBpU9NMmtRD37dQqNtSejm7+Fe6ovjBMZxrIy2Jbm2IR1hYE1otG3WbwxoZxsjMXbxVlQ6oy2A9tgl+pgtdNRrFkWo9ljCt8WWVYxxHIBVTiNaYEq9TO9qm0abX3rU9REmHpV5twGKN78Z+PVfJ52y0coKGqOt2bumaetz7sSVptF2WhBAoIC7hp6fA7U6FEy+KR3pDelLXDKSTmpEmOHU3Q5+5VeAj/xNgIcPfq+WqM6Bdne9g9tvB3ZGHnYwBEYjq20ooOTOqD7aoc5oA8DGHs0HpswIsFQoXiAdSVNFZrTAWQyAuLaPnyNvfNuwze8PAJyO72ky2gb54fvASndGjPZkslCjLd5VwxnGCrTZQjHo04dC5lj6ot/mY+q4g34tv79rJGrfjJbnwPHj9PsioP1ZGn9vxO72b7vdAto3udledgBIEqeh0bYB7Y4/x7yya7QBAsBCOsKB9moP/T7whS/QNg3pCNNqvf/9yuds44JLJIz08zgsMUOMapxaz0WdVNsAmDaoV22MtpI8iKZ0JIrIrmw0Y6MhT4bs+xrQ3sNAY/G3JvLD7bN7wFveAnzbt9E/DKC9yqyLr2IdePJJdl8c3XWkA6ROd1/pSBipjDb9zwTaqr2fyeILhgyBHOAUoP3jj3wrDY4f/jD5japtbw8/9ok34z/nbwbAvGgZBZKXuh3b6irw9M4afrH4ITz8B6xQQ4sWXwJtezJkwJMhs4zZjtkZ7dHExZVt3wq0o8ihAgnsmmfbktX9k9Mvol9awuv5Rx/BI85XwKxaZNMrE6Ck49RFSX7118Boq8/5X/5L+jksWTlyi+e9YHcnhZU558BIBdobz9I1Hzri4vJl4C/+Anjw/vO6tKCqgL09bBZDrK05WFt3cAXrsl9WTcbumoB24jQ12pElHM0StfPKbSQGNoF2s9hJwBduCtD+Qna7+H5ZuxSBMnSXfDjLEAFXr2qfUZKi7joCACOuEWaMtrqgEkWoxozptNicSu0u7bswrDgBNQLFpCPYn9Hm9yfq+pR05ib2vv0s0/S+730NOY3pbqIB7clEuKCYz3mpX1HhrTRFDdilI9DZWrMypAoqiwk9R3O+6/XpOxw8T1Kg49D1f8/3ACdPAr++/Q80oD0ZVUiQwhv2cPIk/e8MTgB7e3IsNTXaPZl0aZN0cKB9dUwnbYusSpMBxgBbJSr0M808ax6WltzMgfbWFqaIcWmHjn1uR0mkYi3dlOPcaf+URaPtN2Re68P82hjtKfVl04pTG+PYi8UtNjnQ1qKvjHyaM5lVf+DIz/apXltV5Dh7XVj4WoH2Zz5DP8+evW5Z4d92uwW0b3JrlY50mj7aNiC9HklW1gbEw6CS0pFNekmiVXqBDx+mbRqMNsOfB1j+1LR3QJGONEP9gHQh4KG/VqA9q+UgaDLaHcV1pCXxRtjmcemIcRzHIYP80VwC7RQddAZeMxmSM9p1jc2Z1NJsYUXuDADyXAPaUUQSlj0MhMxESEcYCu7wazEZbSOJNIplGWpi8eW9FffMkYl0pgZPTNyQsg5Vr/2/Pno//gl+Gc43fD2y136T/GKeY5J5+IVHXiP+tYE1qcErPW3/q7IuCjr+XPFX1i4Hvq9UAG1xWvFVoG2RLnCgvTmJkRcuhthtSkciIHMko60C7X/xhy8kb3i+kjTaV7zpML4Sj+Cj//5T2v9tLhF0nEgrzGLzw5XVPZuM9o/9GPCC7iVkUybtqX0ELQ5Bs3GBuihRIICvqJk8j/I2ODACgKvniTpdOxLiwAF6/8OekSw3GjF3kgHW1oATJ4BnZocV6UhTOy002uPMes0A6c5NjbY1wYoD7bKpS1fHBICBzsbCTWe0P/3xHL9ZU4aj7zOgDTSYvcnnngHAAGsDaLt26QgGBCiZLl0Fj6IIVUonLTTNFh/tbM6YzgWMds503OTX3SyGosqixLjdD1kCaWi3MhuNgDvuIJBt9H3TrUgD2uOxXFAZ89CB1ZISabl0xOY6At0X25afw6ujcreKhr0fPx+mzZ5MPXQ9OZd8/dcDnxrfqeXPjCdAD5SXwYH2Mzi5mNHueU3piAL6hXRkylw6LAtRWVwtAqrKWglZAOm5t1CjbTLaF3BEbHNuvk4F1DhABDDdpsXHaneG07hds25tu+a1pYJIofHYymg33lUDaGtj3D6Mtgq08x1a7PWXaUczxPsC7Y99jBxnv/M7F26mt6KgiXl5eXHuAr+Pk4nVl/351G4B7ZvcxEtoADDhVDCboahcrZKZ2g7GMhSjhu94iwJZQS/bopeEA22tAqWq0Z7QgTjQTrvrIo5lszsCgDisdaBtgH51Um1ltDvSR5tKsFukI4kCaiwDHAD0wzlGczY6iGTIBdKRLMNmtSQ++yDeQKzfoUP0j6KgylmQq38B+LkLgTGpdjpAWscK0G5htGNXY7TVxBvHYWWdg66u0W5LGGPPkOvnHn/br8FzK7wH/wSAsoAAgNEIn8FLUNeyU21gDfU2DYR56cJBJQZtzvQAQMe1e0oDgK8w2m2Whn7oCPBkC81yWdPllG52iHlTOqIwzQAwu0z3+S2vOo/HHo/wr8OflqWKlVbXwGMbJC05/Yz+Qtkq+cUxMKsjPdRsKSQjkiEtGm0ACIOakuSYo0WrFWdaCtBj6mW7Sa0z2pfpXNdvkyvIaBDpjDbrfxuzLtbWSMp4eqIz2q3yhnmlAG1tE3S6btMmzWbvV3WAPEdRNSUqYnHB8lnNIjH8XFRG+9IZ+vnet34IngfUtUOjhCETmvwv/xsAILcB7cq1yih4UaEqa7K7AmjPYgJXFjmAlI444jgmoy3uLS/YZGi0ObjRGG0FaIsy75/4hM7e5TndowdY0qApl7F4lEdBKRZuEmjrfe7AeiWBdlsyJPREWPMZOg6QBFSLoEjnbHuD0VbyMgAgnbno+jKKcc89wJVsiK1Nea9GYxddTIBOR0h0T/ungNGoldFO+p54V225BYLRzodkbWnR2fNzndSs0JjN/pKNC+ncl97hbbUyePRBAdq9Xo1ncYyibr/92+J7vPrrvcd28Ux2iHT5jJ1tMyJYX62EdMQmbRPvasESsVuAti4dYfvp0YVo0VfOaO8yRns1lJ/tA7RFhd1mMLu95Tmd7JEj1yYdAZ738pFbQPsmt1bpSNfRNNomAODtYEcC5CODZqJBFNaS0eZAuyPZyiCom9IRlsC0tkaD5DRe1oG2RdLBXQi4RtsMX6mJQm0VA7tdk9G2SEcUoF0ZdmC89eM5RjmNDvWINNpJYnEd4UB7NCI2l7UfxS/iTcmfyqTIoqDKcZCDrAm0zQSrTofK3QtA0+LAESVSOlJVTSupKAIyXwHahh5Qk44woM11kCcOZfjal8qIxx4G8ot7e3gML9WOlSHGZCuT16P0OZXRnu/NhL+yOZH5gdOsDGksIoPIk2DcItfgrNL5GR3UBrQF08wZ7QvEdL3tu/dw++3A2eFLrEB7dkkO9Je/pL8vtvdMAO3ZTCY1KZfjeUDA8hPapCMAK7CTgTxqESDw9eesVn4UE7Mxefd6BtC+QtutnZDRmLATaKwSn9g2JokA2tuzDnYnngDArWBQA9oGMBqy6p2zmXSSUMYwUWCHsWB55TU02uKauR1eS6ErldGe7tDP++8Yy8Ir8BpMFk+mA0Ds77veJe6JCbQ1a9HJRLw/No02l1HwZEj1GTUYbSNxmV8PIIF2YYT6HYf6kwa0+bg9iBBFJAOpygr4j/9R7pi9+/+H/1b0MMLkM3pCpMloA+QoIoD2jC8uDEb7gIMrOIA6naLCAo22Kh3xmmg8ZgWOiikrdBI1x35AausnmY+OL+VAL3wh/fzC7mGBwrbHAVbcHcB1sbJCQa+N8DCwt9fK7sZdX0jxxNyrnMvyMs07QmphYbRN2Y2tErLrArE3R5r7StLlPoz21hbO4ygA4JWvdPBs525aRCoyoHSX7snJw3OM8gTZ7lRGVfNmnwSA5RXWb8fjxa4j6NO7atTt8H26npkro2l5ViHAHE6HLkSTjnCvbe46sk4fZuFgX402z29QzGX2bybQfvhhvb4CANQ1/vQTa/i3t/0b+rutiNvzpN0C2je5FW3JkD2FMaqaTBtvh7oSaJ9YaZYgDcMav4u34N2/tSp8YlUJhSgPrQJtptXqdhmwjZY0oG0mjAEEtDNE+0tHMrQz2op+zlawBqCQtdRouw3pCAD04wKjugvkOca7JWq4QvIhrtEA2udwG06sjgXI+2J5pwa0R04fSSIHH5FUI4C2ntXf6QBpEdLnZYmKA+1Irz0cxp6IOBCL3wRgM7+na7SNZBeAsX5m1a5hB69+rRx19zCQ2rTRSAPah0BAZWObuUQYyWsqoz0fZbKQiQmMQqfhOmKtDCk02k3GaGWFLP/Oz+lhhJjbpSO1tPebXqIBPDm2Rjba0XHKLjdKr6U//NPi98vnCi2EaHPgIAs5H0U6t/ruAooX+nQqdOkNRjtyMEcAbG6So0ibdGQmE+UaCWO8YiYH2pv0uQq0o46nFzvZ2UEJF1vjEKurMjn/NG4nLesiC7q8lrkFnnkui4G24wChX9K5pCkB7bYE0DmkFMkACUHgaPkH013q28kw1IG2wWiPZ3JHH/j5R1G/853Ae95D19UCtIWkgEcUbNIRLAPjsSgipvZ/wWjXVLzIPA6gLIzz9mtWyRFAAdrDWGq4j9wu8kMACKD9fQ+/ARP08MyndOawrN1GFKWbVBZG2wDah1zkCLG7Ve7DaMvvmTIjQCaxc1u4hr0fZ7TZNJTmPrqhBNr3MGv4x+u7xbVuTGKs+jTmOQ5JIEbBis5om7kFHRczJKhTRaOtzEOeB6x0M2KARyMak415KElICsN9vyuLuwlAErs0D63HaZOOnAdF297wBmCU+vj80ddrWZEp6/9HDtEF7mIoZZ0tjl+9gSdAtBiflOfs+1Sfgm/D32e+KHQchhPCoSy6NS8pJynRGW1do60D7VlneV9G+8sG2n/zN8BDDwE/8iP6NufO4ZvSP8Dbz72d/n6eF7e5BbRvcitze6g5SRxMHZ4M2Uwk4u1gVwLkY8sWRpu9AD/4njuFN6gKODsdh9wxFKC9M0swCKdwXQa0g4F42W0JY4B0IZDSEbvriCYdMTXafHExm6GyADBAB9pUYrc5wPU7pQAkZ67SLHr8uJ7/MIZScnk8xrM4hvtu38Ply8A//+fA9rwnE0+KAmP0xcqfztUR+kag6ZmbJECaB8RI7O2hLCrSXwc60I5iR/PRtjLabkeRjujgVivqwYE2YxK81SV8zTfJhJpdDCUAM4D2qseYz3FE4UODgVQZ7ezKrlIxsMloC402uxST3fUjVzLaFsbI84DVZIrzBWmXImQt0hGF0WbWlfGRFRw8CFwJjtA9e9/7tO+lT0vm83KxqhXAsC1oVUmHZJpbPKWnU+FG0YxckBb/8//3Np7EKU0uoB2n8gWj6utdRYJbrtHe8uAjx3BFsZjreHpp+l2yViQXCwAAIABJREFUAKxrB4cOGUA7TRmjbWdd89xeUAtgZcvR1zXa5uKCM7OTCUkX2jTamQNUFZPU6NcspEic0b5GoD3J5P1/sP4TvAc/IDRJuVGZUwPak0mrFj8OCgG0bWF6TV+dZQ3Pe0BZxBSwJr3y/ag+2tkO/YyGsbxnK4abEAOfm2MCNVc+rfvIm8mQACvCY2i0zcjqgcN0gVeuOlSwpo3RXiAdASTQln3brtEes/oHkzxEN5IOGydOENN8GrcLnfZm2sFaKCN2gwGw5y2TRptLJEz/d8a8zsdS2mb27QGfP8ZjKxHgukAnLOS9sxAOANAJcqRFuNB1hJNpAICtLVyIbkeSAG99K/3r/3Ie1J7zlLmOHD5M+9rBElnWAa2Jy/1lH2P0UI/GEm8YUcZ+XBARMx43pCMAW8R4ywqjXZN3OtvIJh3h89DgIF3sLFnZF2jz6FYDaD/xBNkqmQC5rpldlA8h1Aea5esVnXu5tKovUp+H7RbQvsmtyGtrARhVIlFUbkNHydvBvgwrqdII0ZRYH/erbTDavu7AsTnvYS1J5Xn4fRrcyrJdOsIq9UnpiP65an3Vymj3vX0ZbcH0ZxkxCTbpSLeiQWMywZktQscnT8rKfwC7t/yaRyM8i2M4driA4wDHjtG/z213icIvaFDlE7J2HqKsttOYvKua6a93d1EVleazzVsYsqQ+5pduMtpJAkyVkJ0pHREOHehLjfY4o7DeyjJe/3rg3e+mbfYwEANdtbOHT+M+eb4vJ8poA2T9llcLGO1zV1pLc/u+g8IJsLAEe+AKOUDbc17vpThfk16xVTpSyUqVs6u04Im7Hg4cAC6Pu8CpU8Dv/Z72vXRH+ipfvv2VwK/9GvChDwFgSXIWRhsAZmllTWoC6Bk1fLSNiSxMiGm+9x/QfQ7achgQodhLxX1SW6/vYOz0JaO9F2DN3dJyNyKu+eeT986O0H0ePizzLjaxKsCg3wYG8xrV3D5593q0WK3TqZRRGOAp4onY+zLaTmt/ChQ9PwBMR8RyJkuRBNrL67p0pCgwKSNtP5/C/cKZZCGjPZmIqncmAFvu5QS0RyOUZd0otGQ6hpR1M1ogpCO1K/JMzHsbhnplSAG0lzvyGMsHdaA9HqNUpudL5wtyGmLNZsUpgPYiRvsonfCVTRdV7bYmQ6oA3SodYTk8wt7PkI4I3fOUAe0iQieW44/nAf2kpHGORaE2sy5WY+mh3u8De84SERs8EuM3I2EAJR23LRDFwnk0sgJtAOhGhQTjbUA7LJCWoVUeatqCAiBG2z+BI0fIZfXFLwY+PPsara5DOqLrOnIb7WvntpcQWaBcsw1ol/Ax281anaAGXclom8XKAALau64CtOc1AsV9jCdU7mIoGW1m79cf0v6yZGlfoM2lQw2g/f73E3h+05v0/6v0+6lT4t9XN109X1jRZ2+/4BWyuMHztN0C2je5lS0ATEg6ONB27Yx2t6MMfJa3fTeVMxfP7tcZbSD1+5LRLgpslMtY683kebg90sXxAiM2oB07GqPd5qOdZTLRocFo9919XUeSnqdJR2wD3PKwFJq0Z3Yp5nvihGTzHIdZ77EBYXI1xTZWcOwoHY9ZSeNZ3EYDQ1FgiliwEHTfWIl1wWjrWldt8mZA20VlBdqLNNq9HjB2mHSkrhvXzAsSbGNZMgnjjNgGFu/+5m+mbVSgffrpCmP08YavIXD+IuaIt4E1YHubuUS0MNrnN1AXpbVioO9DAO2yJXIh5CUt0hEAWO9nOMfCqK1Au45QTwiUTjfpZxxTYciNDQfFXfc0tLucEQWAy0M2MH/sYwD4YsmuI55N61btdBw7inTEPnlHHeYuo9wntbkuEHhkkVmM6B1qMNo9YOwOJNAexVgLdM1jGBkVM3d3BdA+csTQUmaZldFWWVebvpSfSw0X6bhqrVwbBrVktKt215FZ4VF/sfYnB4Wm0WaM9npPAu3VAw3QOUEXB3oTjJaO4S48oTFtxK7La3ZdII4qKR1pYV2X+nJcKUunkRSueWBnGfPIbrm3zCWICuM0gbYmHVGANl/wftp7WYPRfhz3iD8vDe8mi1KWHFbYpCM8Kqey+CbQvo0Qz5WtABXapSMqcLZKR1gBNu5WYTLngtFmkp+0itFN9P7S75aCUCgKYDvvYzWRRNNgAIwc+lwuEPXzEO/zuLAm8QKSNOIabds81EtKwWi3SkfCAtMqsjqLuS4QhVVDo30Wx4VT3UtfCjw+PalLR8YlHFQ4eBsNDrvf9UOU7PvEE+1AmyUjjjbnrRH0frdqaLTVqWo4BHadoQ60lQJU3DfgIg5LjTYvwc4iwbNouC/Q5txXA2g/9RT9/NzndFZbBdp33YVNrGCCDv7Hs+/EW9+i9B+F0d48et8tRvvvWitzewGYTgeY1iw72uJBKxrrkQcsXsMAmfjz1ga0p66UQPDEwLVBJj93GGrc2GiUL+YtZnZf1ZQx2m3SkbmDqmphtLuOcB1pq0JmAm2bRntlmRi7ejzBmfEqIneOgweBo0dpvn7b23Qm4dnT9LIeO0k7E4w2biM2oSgwRaIB7W4XmCiSGwJpukYbkEC7zGsr0I4ipWCN5Zp7PWBU91nlg7KRgNjtAr5fk6MIZ7Qncw1oa2wDGwQf+wI9kH/6o2O8733Av/pXtM0G1oCdnUYURWO0L262Mp1BABROyJIh6X+Litq0Au0hFaABGNA2Rl7h7Tqm85gxMMKBdl0DG4MXNGzf0hFtf/QocPmKSysVJjswdfZ8fwDTTud25pb3/UXJkGHEmGblPpkt8ktkiETItcFo94AxJKN9Ne1gLdLzMsj+LUKdNhntI0eMEG+WEevqtbCuRd0+ebPJczyGVa8MAGEgAb2tGqYG+nlSmS0Z0osk0N5jjPaBvsJorzVA5xg9dOMSvZ/9EcRdn8Y4nixcuY1z6ST1vqBzaVArQLvpvqRJR5i0rS1akCMgrTeCJqPNvfXZ+MQXh/FqFw89RKDmXz/9rbqbymiEv8LXij8v/f1/RFHIRx+le2QpBtTtuwIsigRQUzpyiP6+sh2wgjV2pK0mFFqlI6wAG48WqJ7rgMJoZ1R4a1J30O3o++h3awEGeWoFJ4QAJh2p+zqjbVy0NUJlMtqMNMJoRAXCLCy+0LcvYrRZVc0yzazHEVFrhdE+UxzBiRP056lTwNnJGqZXpawzHddIMMXyYbqQnZCFqDY2pCzHTKIe0D0Y7ZRy7jXGp36v3pfR5tISAJjnQOhKDb0A2t4xyWhP6HM+VmRR354MmefAq18NHD+O8Wnq0w2g/dnPSq/sO++k5Oa6lv75DGivYROvwMfxqfo+XD2rJEQqQHvjwIvIc97wmn8+tVtA+yY3CsHkjbeDqp75yNOclfJtMVgPQ5yL78QX8MIbAtpJAqROTwPam1jF2lIpPp+CjU4bG9aEMQCIE5dJR9ig0ga0c1fqWA2g3e2S80U5mS1ktOeIUKYZs/drDvyrq7SfdCPFM9MDONG5KiQmYcit9xLBhj57hq7ntjvpOo9S4jfZK3GgXZuMNpAqGm2avJtAe4pkX+nIvA6YdKQZLej3QYmdaWpNQHQcwopbWJWMdppbgbbGaJ+n8zh1X4y3vlU6fexiCEynLNQvgUQcSxlsdmlbAm0biObSEc5om9X+fCj2fva+vb4sB/EQ88Z9ExMzc5fgBG6SENAGgCudkwRGuFa1KMTYescdVOuhOHhUXFhpYbRF0tK0Xa8s8gam01aP+Ch2dEY7aPbbKKDiUpzRbiRD9oCJIlfamPWxnujlx/kExRNisbODiwFNUIcOGVZdWWbV7gowWLrtriNKUaFWRltJ6svrBdIRRAx0+g0WPwiA3FGA9qiAjxz+6lAC7ZV1LbzOGe1eUgE/+IOIX/QCzPy+wmh7DQAsfO8nE5GM2kh67TjiXG0SOu16ZjNmV9jiOgIf1R6NHeY1i7wN1ql5pd7BwQRxTAUgP3LpDpTbu5LRY0D7yMECx48Dl+Y8e5MQaVl7jbyA7sCTiwtLwh4gF9hX9uJGsSztumL58K3SEZbDU3D/ZUM6wt+zcRaK59fp6vvo9xkYTFPxuFcHufI5sFd2DUbbrtGeTWsRcTPfZ7FwbtFoA4rV5mhklf8AFqBtHKfDa2WwQWm+NcbFbEXgybvvpp9Pjg+JLME0BTpIsXSYbtgOmB3OxkartE0sinfLVl16vwdxb5kTrfY+D4fAbiUX+XnuIHQlox2GNO9eDI7LeWiqA+1ZMLAz2g8/DPzVXwHPPovxn/wFnd9//TOZGVnXxGQ/+CDwXd9Fg88730ngWwXazBroi3ghnsFJ4VCEogAef1wcbmPpTvrleZwQeQto3+SWZSzZywK0AWA6qZiOsp3RPjp7GsvYsQJt7ukKsOx+WKQjTkfTK29gDasrlTiPacW+sLHROvBQNrei0W4D2pXfqt0VLPC4as1wTzr0z9m4aJWOrK5TN926NMelbAWHu3va50lCYe85C21duUyDz6E7uuLzQZeZ/O/tAXneArSlLaKpgWww2kU7o13BQznLrWHKfh8YlQxctehYV1YcbAfrmo92gFyMcEFAxU5UoH3+SoguxhgeJcTkulTciAOwvG6G+vk1zUfthUx8H8idQCR38n2rLQjomqvZvCG54W19Rf4vQtagOITFVsr6Q+7DdSr4vizEdLo+SYMsH9z39uh5AHjJS0h+f2Z4n6iuZnOJEM9x5krpiAnAYgczlrhcWbS7gOKXzlpWNSltXvSpGHNGu5kwNqpkMuRm3sdqV7exEvIF5u6A3V1cCE7gwAFu50mOCRkiiijUTkNSoMobxPtsYdcBYDxxZAi+LamPgc5W73BEqEdj1HDtUiQ3lEB7XCLGDBgqQHtpVQfaoxEm6ApZXZIAM09ltJvnIlyENEbbSO6MmaPLaMQqwbbYFXJGe5EsB77Q4puLmDB0MGd5GwCwt0v7GAzp3rzsZUCah3gKSgGX0QgfxVfjVV9V4PBh4OI2O5mtLebd3mRduz0XE5ZcKzTaBusaBMCKs4Uroxg12oG2vyKtQ63SEVZJVFhkGn3bdYFeMMNoTnKwFB10uyZgdATrygMYq0MJ9gYDYFTQmNzmlqNJwRhGazDajDQSSfkWRrvXrRXpiJ3RTqJ6IdAWC3T2Pp/biFHDFYw2B9pP4JTo39NpTUD7AI0lOyVDsRsb+0afRmNHFhEzGW3OWE+njWJlAAPapRx75oUOtAEady+6RyWjzXLC+Fg9DQbUH02T7N/8TQojv/OdGH+BqpvOT5+TlU63t2kMP3UKeO97gUceof9//OM60FZaDRfTtBbfn87lvd9IWMj6eSwfuQW0b3KbzxcD7TS1AwDR1Aw929uuNO7v2pCOKIBxtjHGBD3BZCQJMM3ZuTGrQRsDGXc96txje4ELdVLlbION0QYY0G5LhuTAZ0QaYRt7snKADr55pcC0DNEN9QFB7GNC+9/epHvLw3EAMOxVxO5mmWC01Vvd7RIrrjHalpLZI1C4rCqZRtt4ziLcnJbWKon9Pps8GKNdWcDI8jKw5a4rTEJB+mplVhz0axpIWeju3FaCo84FOKEcoDjQQ5Y1GG3tXAu3NcNdMNoMdAJ2RhugLPk2Rnso6wdZpSMm0J4WAWIvh+NQzY5eD/jgGaZZ5fKRnR3q6wDuY3mgT8UvltIRCzDSSilb3AMAxta5ktG2FXQyNdq7s6i5TUgAWABt01s/ITlZPabJblR2MOi2u6Twa77g3oYjrOCc4xBzLhjtuukBfC1AW7BkqSsq15pAO4qkow5Vw2zRaCNGuUfXZJMi5a5kd6dphQRTII51oG3RaPM+EsfA1O3ojLZFr9yw9zPuf8QdXQSjbb/3oqKv5TiqdIRfswk6w5CVWeeM9h7tg9/zl72Mfn4K9wv5yO7VOc7gJF72Cg9HjwJnLzDzYwZsbPKGbheYOIvlMgBwwN/ClXEXZe22+2gfWBa/24B2nBBLzKvWmtIRABhGGXaLDrLdGSp44vnx1h86DUZ7bVmv8LmXUxGZKreDTo3RLuznEiWOIv+xSxi7XUoG5lEJq0ab1VpoK9KTJIrjV13jzA654nCgfdddtCj+LF4sgHY6ddBxZ+j2HHgesJMlNO9vbLS7jnCgPXHlGGYsLgZDl+7tdNouHSk6CtB2ERrk36FDwEUckhrtqZSydLtAGi4R8aEuigHytH7pS4Gf/3mMX/+ttH+WRA1A5tlwBuXUKRrgH31UA9qlMexOS1ZbYjaTzD+ADZ/pXJ7HCZG3gPZNbtncHhoX0oO0bjhaaE0djfYB2mkZwXMrbbMkYYVVGGDcvECr77UDSinxjH2BOWPY2PW4S9vwEromGNSAdl6Q1Z3bDKUB9C5XLQOcWIAw434bw7J6iO7l5pUK0ypEHNpZSr4o2Nqmc11ekec8HDAGeEbFWaZV1GC0J1WCeiSBtnpfObA5j6NMOlJbpSMqA2mTy/T7oOI7inTEHNRXVoAtR9Foz8oGqBkMHVo4cEZ7p4fbfN0STWTbZxnyqgmMhONB7rZWkhNAW5VRtAC5fFowDaTFxrGrWNYtkI6MMx+oKswKH7EvXXVe/3rgjx47RvaKCtDmjDYH2k86pxYy2iKylLUz2knC8hhYMqQNaAu/dNYWAm0mv/LD5nEAsiYrCqqM2evq/UU8I8ZoVzt7eCS7TxT8APTnbPNXVuUNbWyckI5MfaFeaGi0IyjSkaZ3uMoAtwFtwWizCX6a1kjcDHAccbzfPPMqek/5xDweYwsrYrGWJMDMkS5DRe01CgaJ5ObJxJq8BpBzjNCTW2ReWlRoOm04BPHrAQxG2wK0556swrc3dhE4uRgr7rmHito8hpcKoP3Z0yxS8zIf990HPPGEg/HSbQS089xqI6jZ+3GgHTbnkAPBNi6nPexVPQzDWeNzAPAPyKqzVukIk90Ij/ioOXAPkwy7ZQ+TDZJOdUxGeyj9oLe3qC+pY3a/D2LE81wU4DElXGrORRvojDuyeqQtKgGQA9AEXZQjHpVoB9olK9Jj02inHnOLGo9xtiLNIgfa3S5wz7EJPo5XiEViOvPQ8TLhbf2uf+HgscHXLmS0VZmXID88894uBtrDITDKE2F2MC9chJ6F0S4PSClM7iB0ifzo94GRx15Is0z6lStkh+Q4GC2TbkZzTuKaxcOHsbUFnL/o4j8d+TGqjqoAbVNyPUVCYNwA2psTlshzi9H+u9OyzFksHZmyinVe82UHsC/Q/vCHgcOgzPNNrCLydRAgKhgyoL1xnibW1UMsyfIAcOa8jws4TAyYJWEMsADtNukIIlRZQeyuEV8XjHYKSgy0DHBCyzeyDxgAsHqURtPNTZK9JLEdPAlGe89Dz5noA8tA8Z0uCkyrpnSkrD3kzMKISinLz7nO7qz3goXSEcESTyurLKffB8Z5jCqdAnluZaZWVoDtekky2lkTaA+XHOwp1kvnRkMcjXRmgTsDENPZTORyHHLGmCOQAMx0FAkY0E5T6cBh0XEDVMGyNXIxkDdzIaPNNKazKkASyIH/Na8Bzm/EuIyDVqD9ghfQPp7MjlPfn0yYHVsLo10EC6QjDMixxYVp0QgQGzqBfFd3p82KDHFE7C7vU2YypBgTxqVQevW7LTrhKf3/kfNHcDlfxRvfqB6nlq4jLWXPAca6MkbbBCzqQocvqBrSkcgVnt4E9NqlI9LSUN8HMdqRAMnTqYPEY8CFnfdPfPA19AtjymabEzyJu/Ciu+kexDEwrRPJaMMiHekAEwa029jdqONK676WXBXuFy0Z7ZZiQPCVxUXTgm7uSqA9Sj30vVQMl2EILA9KzWnoM88SkHjxSxy84hUka32s8/co7N5SXCpJgBmLkPBrNllXADgQ7eKJ0WEUCLBm5ATwpjHalvwDLi0U75CN0e7k2MUQ4wt0TTyJj7f+sicYbZ7U3F2SHWYwAOqaAHA1sddzEEA7c9qBdtcT0pGytkfcugxoVyP2DG3JkF0CewJo25KbXWatu71Npe4hc0wA4IF7UzyKB1DvMqCdeUhYxUyeVvXv6n9M+VMt45NgtKe+1f8dIAvACXqoJu1AGyCwDgDzwkPoNRnty/NlAZDzwhHzkHDPAnSgXde0WGRJQvyarIz2oUNYXSVXsP/+iZ/CFy8OoCaITIyuWcJH/tcEtLch++fmJihccAto/91p85wBiTagPXPI3/cGgfbrXgf80/BdAMhRwga0p2UoCx5cpo67eoRmwbe/nV6YX8IPCUbbxkBGPTp/3tkXAu28tIIRwWinTmvIjt8XyZw3NhFAe2uDJr0ktoMnruHaHgdYDnT3hsHQBNo6oy0WBczXtKi9RvLIYACcDe9g0pF9GO2stoJODmh4FUpb4s3yMrBVDiWjnVUIDFAzGDjY9ZYJAFfAhekSbutsadvwhKW2UD8gnTE4k2NWXhOOIorVnalXFoxpVlrLVANAsg/QFkAPPdLgIUEcyL7NK8l9Hi+yAu1Oh5LXnxixMOLly3Y3CkVrL2QUFmZKMGAtPvNh4mEG2YF2p02NNnegEfZ+ccuYMKkw2sq1+yCOYzDa//XKvQCo0hxvHNC3MdoaGGxhtMXkPQusyVOA4p6RpqzsvJ05V/uTbeGWOwrQnjkCaKhVlgt4Qj7y+FMBSvi4737alyhupDDaZmEcylWhpOM20BmGDjLBdLbkqqjFiyzg1nEAz62QI1BsHC2MtiPJj71pgEGgVzhNYmg+/o9fWELPGeP4ceDlL6dtPu5+hWC0bTaCwrlnlC9mtOMRLmXEWK937EDbPyj9P73lQePzKCHwmnPnHivQLghoX6Lr5v7LvPUHrmC0020Cr51lGRlSk77l+NTCaGeOjMQ0NNrOvhrtbt/DGL3W4wAsSrKA0V5dBTa5W9Rkgi2sIPBK7Z1+4P4Sl3AYF85Qn0/nnihNz92xlnu5zmi3ybymXqucr79ML+94t2yVjgDA3pwKms1LtxGhGg6BeR2K3CdivUtxDuOaTZrMcpJ2SDlQWF/H7q5wWqUFOgfaCqOttrOTVY3RVmruiTY9v9VgtMdjENNy+nTzC8+Tdgto3+SWzRcz2unMJdeRtjuvAu2oGY4GgF5Ak+UmVhEG+suRJKxUOAOU21dp9Fm5jRDGHXcAx4/VZBHGgbaN0e7RW8k1sybTrFpfVfPCCrQ1RrslGVKA8XE7o71yhDHaHGgn+ufi3k7pu9tphJVIn0CIAValI2GD0QZITw6wZEhjIjt+HDjrnhRAeyGjPaspTGmRjgCK1tvi2bqyAuwVXRS7PCMcDVAjgARLJCpqH0f6+uKiAbR9C2D0qAhJGzASjiIM0NsSAwWQy0rrNQNAZyiBtU06ojHaW1uYIUYcNoH247hHJkMqQDtJgHvvBT5/kTEdzOvZnFTVCm5C0mFhtLkVZ1XVdkY71r+zvlRYttGBdtBpeusDBLTHm6wYRF/bRKvACgCX0z56wUz4rfNtpEbbb484wEeV7pMMmYXt9n48AXQ8JqBtTMxcL64DbZt0JJBAe+4iYQsqVeq5CZkQ+emnqXPc9wDdvyQBZlVIk7qoQGlotLssKVxltE0v9AiYO6Gixbcw2nzRxT2yLQSJ77FFDK+ia0pUQmDuRAJoj7IA/WiubaO6pADA1sjHergLxyHZWr8PfMm5g4A2q0Bp5s0IoD0pFwPtjhwn2oB2cFj6f7onTzQ+j7sku8nZZdiOM+iV2MMAo8vUF3pDw+u5T9aV81EmpIPJSqJ9DhDQFoy26SjCgfbcbXXgEEB7OiXCxyYdGXo0Jow4c97YBJ2ugyk6UqtsLC7W1oDNigogIU2xhRWs9HNtvDx+B+34ykV6PtO5hw6L0H7mM/QebvvrC11HhHQkU6pUmoz2Ev1jtFfTu6LcK0Ay2rsgL+156SM0SDvhhT6hAlSq01CvxySQgM5oc4vK9XW84x3yT006cvEi0OlQxElpp6eHNKCtMtp8upiOS2A2o/MGvTfjMUifc+GC/P7zrN0C2je5zXNnMaOduaSLvhZGm78NRuOD9AbWEIVNAJaXHjFCkwl2tunzpaNyv0Lfy6udWZjOuENdg9v3NsKuCqNd7sdoZx6qNu0ul460AHqAQE0XY2xuUbgyNkCOYLR5nYCsg+VY1x4Ol11xzXVOSZU2oD2ZsCqwCBph7+PHgbPVMbKA2kc6Mp9VpNF2FwDtvT0ro81cjQSezAvXCrSnDEhwosDU9ybc1moBox3uA4yiCJjVkWC0bc9ZSBNmZatPbaIA7QjZYqC9ucmAtjwWgY0ajzv3ynjk5iamSBBFNTyPKq+d3ehij5USt7G7qk1jW2JgHMtrbmO01TXwG/AB/PbPNRNx4oTC0d/+2DvoPrUx2mmN0QYD2oOWBS0D2ltZB6uJzobGCaTrCNyGpMN1qR/mCBaGvQFgnIftyZCJBNo2jTBARTtmiEV/MhcxQcAWbgJoe0jYguoHfgD4xm+k7a7ggADaXzjXQ4A57nwJPbw4VqJ2ouy5ZSFaJws12qrnfVtkL45l1dkCzUUMXVNNGm3OaDeYcyBTgPbePMEgNoC24pICMHlJKLcZDkERLIXRNvNm1P4krjlqPqQDXSl+Xe+mjc8BwD+8Ln73YoujTpciOtPcR4LUGooc9mtitK/SfektNYE2QHaH6W6OCDN4A2WeYqzrCH1ZOK2N0Z67rZInQTiwMcFGcnWXfNRwMd7hCYjNjZIu/W88YQtRG6OdDyihkgPtgeFks0QPabpNY0+aB+gk1HeHQ2K1t7BMxeRaGO0wJM/rURa2M9p8kbJX07sCaPOdVothPMa88hAa75CwXE0dIMswR4jQV6QjqUcnbQPaBw6oDny6dOTSJeDQIVy+ovff09mRVqB9LwXyBNCeMYvi9XX2Wp08Se4n587h+dhuAe2b3LLctQIJMcFnDnO0+DKANtNxtgFtQDqP7OywJJM1OSgMhw52nSVRpdIGjPgANpnS92zMlOvWjNG2A23BaM88ptFuXosA2vzWi/LtAAAgAElEQVQ4LfmfK94utncdzJAIS0BzH+mMDrA972Glm2nbDJc94TqSFw4qeHbpSO6jmrGschujnR8iC6h9pCNZtj+jXe/skgVaS1Y5Z/nz0mmU+BbFESYTEXKPu82QKS8GZGMgAVlWW7BxFtC/k3dRT1KUlV2vLKQj86o12ShZksg0dIrGg9akIwJoy/04DnD33Q6e8k5J68rLl5F6A9Hn+WD8OdzLLOiaunRVOtLGaAsLzOkUVYvriKp8+U787zhw0LZAdPElvABn5pRJ6yctjPbcxegqPUST9RNSpFkFzGbYrJax0tP7tijIwbTTJgADAN8l5rct7B1F5LoxyUOUlR1ok3SEkgdt0hFA6sUlM2iTIgUCdE7zQORc9PvAT/0UbXcFB8Rz3tlzseTsCsCRJMCsZNUl05Qx2vp5qEBbSEcMBlLYFS4oIibYUOavbB4HAHyPLWJaGG3STkupy6iI0U8MyV/P1azhRrMA/USyc8MhsIslodEmj/IWoD2pFjLaL1jeFr+v9+3JkMGSnIdsY3Kc0LF36gE6SK1Z7MMBA9o8WrOs938xFo6o8FQHqaadUqUj1Zgnml4Do22cbxQBOUKUk1mrtG3AFgE7u+2ET6dP24i5yjiXtTWyOd3dhQTaS4aPP2PsZ7t0T9Iy0tyvVlaoQiZms9YqrgDQZ9aJbRptsUgZOQsZ7T1QZdq88hAGzSrG4nr5HOJLx5zxGFSk4ktfkl/isj4mHXnwQeDB182ajPbhwzxnHW96E7CaTHC6uM0qHXnFK4B/+A/p9+mExkGeiL62xl4ZnnH6zDONe/V8aLeA9k1u83y/ZEinlQ0CoIs0/1/2zjw8sqM897/qfd8kjTSrZ8b24AVjG9sstjEXmyUsCYshxGwBwhIgYEK4QEICIRcI5AZIQoCEAAkhLGHLcoNJAgQSSAKJISxmMYuNx+PZJI1G6la3Wr3U/aPO2qeq1R5JI2mm3ueZRzPqM93Vp+pUvfXW+32fgWgXdqqnaIbxiLskeDROvc7cfJwYvcjbLoiyU2DEEAzpPJTNthoiuihsl6T1uz2t6uepxEtxcx5t16PtTF46bxxAJbnI0QUnL3beoGi3YyAlc70i1QEloVSJ0SZDu9GhtRwPfXaoreT9oKaBCW5yEma7Fbr1Fr0VrCPLTml6XdYRcFSaORUQYzoabDQAKen0YpHKg9msQySaTZ9oF8ODyssfu7ioVf1AldVukzYuZNUqdPoJWi0U6dT0c8g6YljIcpWAdSQaNxhRtHVe/FoN5mJjvqJ97BjNbI2cs/Fyifb3uMgLXtMVGImJfigfbqRkc0YFxnaby2pzoXk+gs9dhiVtach0VnhHnACJXPiLu89Yi6zvY61EySA4aadPnuQEtVBRD1AnPiF7g2Zu8ewNBo82KL/+cidmzjqSgrYIjqfo57gqsem0QCnaCcWupKTVS4b6eZtTGO8427zAwEYDCnFfxc9k1KldjxgsLGgL43hB4c2msWJgOg1tmQqkOdWcuOVinmhhUrTdOAb3O+vSOHoB6u02C7JIqTBItIUqmOUcqdWXM6HAWFVgRMUveIq2wTrSauF/Z42ifdn2Y97fx2vR7wzhzWeQoLlwx/8cVTPRriirxYkZRwWthcd/mGj3I0Q7aB3xgnhNebQ7cb9gzUAfedlwmj2nSE/0ea5U1fvOLqiBpM064hDtulNWfvDejjm29pmFlE+0qwOZeRwxpLXQgX6fZj9DtuDfu1oNTnQKav7ygiE1fvHEMq1uwq8MaVC06w3h1c0IrncR60g/EbGhetYRh2grRVtdUyg4+8brr1cZGpxn1VW0v3znLr79bWVV9VKhBoMhp6Y8q/brXw+X7zjGHXKfT8YDivb73qfeB5w4rICiPTYWsI6ASi24CWGJ9lqh34ePfIT2QnuodaTZTw8n2kFFu1LRXlLZq3yokhjjtRUU7UaCSrwe8omVAhk4elJEyAgEFG13UtEtzC5JM1hHfJU4aZzgPKK95BBtjZIAUEm3OLKk7kd2QLn1NxeqauMcVarl8ELm7eAX/DziwYnHvdVzVP2MCQOTrZuL/MR83OjR9hTIZbQ2iiDR7p9c0H5nj2hLZUDr9ONaItGSmbCiXQhf5FVEcxVInaLtVPszqXGejWUxqTYOaDyqrnVkWToWIV0wpN82HdFOpSAR74c92ulocM4CpbCinap6/eiWDT5BzVhURQjIOZkkXEU7WrJZ/VTV5vRBcsHvkKWl/VKZXJw+/nsPKqqep5aM52MdJNrB4FpOnmSWMWqVcHu8CocOAdbaGxJyqKINkIr3aPcTdHsQoxct0pNyPM2uR1uzcUunHKJtOCFJJqEjEyq7QLsdCUoOEW1XAV6MUUz6ymuoGqZj6dClulvuJ+k02r6NIhXdxPSJ0110KprqTvZyYUVbbx1x0vs19JuLXE4plzQa0GhQp0gxPzhvC5puDubFReoyT3FAHJnv5JSvzdtQGRTtINHW9PNkzd+o5crRDaL7nVy4JCcItw/mqJITregFQLmmPvvwtOqc4nhYFfKIdF3QXIwS7ZCivZJHmzTdtj6g3iPai12jdcRdH0401HM8jGgvtNX30Hm0AWYbaZ9o1/R9tNTowsICLbLkAgJJtQonlvJqbTak9wPIJrs0uyljytVgrm0d0Q5ZRxYXWe4nI0Tbs450kv4akvSJdqMB3HSTUgFuuUVdPD1NiwzXPUWlWjnnHBU4Pmgd6U3u8Ij25CTsrDQ5ypQ/tweIdqEQPq3REu3du9Xkbon2GQ4h4FnPoj3XHKpof5In8x9cMxrRNijauy8uqbzVwL695qwKNBqcbCapJMLhux5hWTIfpXlEe9m8w0+nXaLdH+7RXk7SZ3gwZKPtfI7BOlLJLnOkrx7ebEFfgbJFlvbdx2mRo1qOkjSA+XpMS7RdknaUKb/wxIAy5SoWs400nY5Q1RpNwZDLQru58AJZKNI7qSYVo6JNAY4fVxOcJgdzq5dCNhZZWlRjIVMctCYIz8cdnCQH2xsiRiaivZRRgYEa0hmyjsjY0A0VEJnQQT0++ZxTBtkl2gNKWrkM87IUUrQXEjXvvT0bEgWjRxsgl+mFC0+YjuCbkn5faoPkgop2lpZe0c6Fx6nxc8j61pEB1S84npifV4p2LXSJf3LhZLHRKtqJ4R5tgFTCsREt97U2Cs9q4RJtDUfzAkANRXrSacf2AXD8uBPc7N+XSkVNnceTu3xFeylOIe2Tw+AGxUS0g2qpyTribWJabhCvLobEqRLq+dL1G81OIEVmIh0l2q1uUuXobzRYoESppAtudjzaMzOKjAeydJTLMN92HghHxTelumstCY9oD8YFOF8q/MEaBImbmw1D91knqKk86BqUtqmL7plVnzc4tqtO3PLJxaTKp05LS7TrFL17O3jiGSqS5OTa1nm0AZaafafib7QPPaLdzGg/B/xNSd0h2oP331O0mzkv60htXL8xaNW79GdOsEQ2tNmp1WCunVVFiYZsinOpLq1uir6zKR5EkGgvkUYgQ1rAoHVkWSZIairXgp8JqkMStx6aax356x/dny9wvV/1cXqau7P38d5j+/ZAEHWrpZT6k3V2/uWbefGL1TUTEzBR6TDNBHLBJ9ruNJ/PhzeRWqKdTqsPs9aRMxxCQKGgjmA0irY7n32WxwBwpF4YfAeFINEeTEHgIHX+OWxDeaH27dfn5nWJ9lwrQzUdDngplWC+X0S2llT54mFEu+MQYJ2inXI92p3hxWhGULQbXTV5GRXtQofjKKI96EUObi7qB5X/0C1v7CJEtLt+9gIXbq7TY0x62T4GrSOuYjGzmFUeSuqRfvYW735cTYIDX8dLzUSB3nxD+zkhon30KEtktCkN+8RZXuywdFKRmkw5rBh5QUBuOjadou0UITEd9XtKf6+oTi40/exZRxyirVOMQgVPNcfZoIj2Inn6Myc4wnbGqgNFekpO2WBH9ThyWPL52cu4+mr1eiwG+ZyjinuKdvRzshmn8MSSu5AZVDJH0dZ95+CilWFJq2gPZiYZVIhDRNv1sY6F38cbTzJBf3rWWbw1Aasi6yvamk2xZ28wFP4ASCdUYGx3JaLtkluNd9S1sfgbt4HsLBOKzHSJw+HDimgHYi5iMfWcHU/s8Pq50U5RyPjtGUXR9p73ZtKYXzlUxdWUfSkDt8or2P+Jt2oDEEGdfHVJ+Cckyej81JcxlruC/swJ6pQiQa+hrCPT09QpUqj6XypItOX8gortMOVlb4uh1hFyOb7PBfwbDzES7eBY1Xq0A0Q7F1+OXgCUp1SDDtXLCPrkxsLpotzMOScW0zSbKEU7sOaFso4YsuXEYpBK9NSYcyoUmxTtpZZ74hZtqzvPuURbt955RLub0X6Op2hTY/nILA2K1CYMp1iLPVpHVLR70FZXqyki3xlSxRUgm+rTkml6nb42hsS7d4txJ1VqN9SnuRzE49IPhiRFKqUXhVyivUzK21wXCtDrwS88O8PD+YIfuT89zcHyJd57XH65SqO5LBzryLFjzFHlWEO9+YEDSqPYVuuwRNYLnA3m0dYR7TZpUilJqeQV4lQBkVbRPgtQLNImrQ2GHExJ961D42gRJNq66EGAffsUmQf23Tdc1zZkHanXOdnOUhnMwFFWyd+bTVa2jjgEWEu0XT9mV2rJSCwG2WSHRZlVwZDDPNpO8Q+jR7vok66gDSH4Hi2yNO5R5WLzxfD7eAtvI85SN6poZzJQznc4ypRvHRkgYB7RbuZotJMUxGKEPQXTHvZ6DLWOeETbZB1xiHaLLDlDSsPWYp+lOTU5ZcphkuYR7aGKthhKtD3ViQq91vJQot1ZluY82sH267wjqO+9SJ4f3Z3hJFWuum94g1guQ7OfVYF2vR5/M/sQ2r0kL3+5f42nii8tGW0UuYxbStlRwEzWkSWM+ZVHUbQz2XC/1uvh10Pjdq6ryEg1vFkKjqeFgyfpE48s3pmM7502K9phoq21jjgxF91l/eKdTsOyTCJPzNEjoa0GmHHKXXvjaeB5nppSRUimmfDGtpvNwUWlAifjY56iXe+kQ37l4H3zibZhY91MGjNwBKu4miqaumPhzuak8d4mUwMebYNFqEmOpYNKIMkXo2S8KRXRltMzNChQDCjA5bJfFMmdNwbtZN596SSGE+1slgu4nYfwFSPRXgkhj3bSQLR3qDn9nvYYBRqIVLjB7twy18rQbMUi1pF0Wo3JkEc7GR23maRDtNsrE+2ejGuXVU/RbjkkWpNpxSPa/bz2c7wTT8aYu1v1UW3SnGmoeUStVbmaf3Tn3ROqQ4m2eyrX73S1c7J3GtBSRNvNVe9CCCgV+opoOyQ6ldKvQ66dr0PSm48G8/27Zdo5fpy7swcA+N73VFpWL7tPqwVHjnhxK+95D3zzm+q/TVTVfDN91Jl3AtaRENFuxzxFO532Cf/yMsqnYon2WYBCQQ1YjaKdSMDOqj5naQj5/MrXXHYZ8aK6bt9+QwYO1zrSyVPJhSdC79ioETMqkO7kdLKnmKEupbf7ADUoUIjro9fzqY6aEHp9rVqdSqnczA3Uk2tUtAN29exA0F8qpTKgNMmxeFg98IVK+Bpv0mjpFW2AqfGuso64lo6BCc6bSPsV6ksJiiLan14wJClltRhiHenPKvU9ljaUI3eIdpOcMdNKa7HvRbBnSunINeFgyEhzSaXF0OA1zzpChX5reXgwZMch2joVOXivdZ4D1OaoQYGv3a0KGTzo8vC49TybC8DsLDNSdUjQQ+qSdS8bheaoP5dTFTNdRVsXvAbqCN5U6XIUj3aQwzw//xGuvTb8ekjRnu8rMpIPE59gGs3Zu9Ti7VZ5dRHcUJkUbddH3F8yH0e7MRdK0db70pdlks5JNe6TGc17ZFXgkynoMmjR6hw6RocU+VJ0UzwvKmpn0unQ6OcoBA737pWivewTMF3WEVCpE7v9aIYaCI9bXe5qUJagLgnPijR4b4Mnbq1DKmXh4BymiLbqw9Z0gz7x0OlGuayq97VJ0T3pbNAThnEbTF1pINrav2vwvOfpf+/2QYMiuaQ+d7E7Bx9lSitKeCryUo5m2yHaA8S/VOirudKgaINPtLvtvjbPv2cvaUl66Ocnry0omT2WiT7PrsBTRw3GSO5qZ4zOU+bkYSUSVMYGAtRd60hT0jyqNpLB3OGuyj9H1c+KpKnumU07c1i7q7W2ZTIqi1DdUbSzqejGuVySyjoyO+sQ7fDrIevIiRNK0XbI+OBhe3PGEUWmpzmY2A+oGjIQOAlrNuHoUa/YzI4d/vCbGFfP3vRxyTJJxGWX8qY3Od8jrifamYy/Vn73u3CoegkcPIhnXN9EOO1EWwixWwjxRSHE94QQ3xVC3Oz8viaE+JwQ4kfOz6rzeyGE+CMhxI+FEN8WQtz/dLd5ZAQVbQ2rOW9KTZB7uIv//N0v6t/DQEJCSKW44alKXnWDbV1EPNq9ItVCuJiGFwixmDB61twJYZoJ96tFkM4okrZAiVJSn481l+oqO4AUxDSTpBAqsMNVtHUkAaBS8/9vdkC5FQKyKbXDd7M3BEv5QjhVkYloT070lXVkRh2DDSqdnqLNuFK0Y9HvHMovTjyyiYnFIJ+XStGeUUR7cDHUEe2cIdNKs6kqwQFkKmFTs1d4ZUg6tnQmrGgP9lGIaLeXtWqQ59Hu9NVCpiEsoUXJpGgXBQ1R5BvHd5OnwQUXhF8P2n+YmWGBErl0N/TI5PNC3TfXr6wl/SpncdcpPGEMhpSqIIS2cuoIWUfGA4dWb932tsi9CxHtOsqKNEA0XIXrBDVmDyqCW9tusAgtLmrzv0PARzxM0XYDYzt9EjG9daRP3LMq6aoBptOCtsgYSUKQaDfvUhkKcgOkU2VFKqkd1YkTyq8cIOPBbC1yoU5PY2MJVRU0VAANlrdXaSnNijZgTu+XFCGPti4Y0m3v0j2KaA/GU+Ry0JFJOvUlP6d6zb8mmCXCV7TNnn/PxrIS0R6iaEsJ732v/rXgfcklo8WawJ/HjrCDgojOlfE4VNJNTiwXaLbjyoIy8JAUC04fuh5tnaKdko51pEeCaFtCJ1SGE7d8HuJ0PaIdz0WVJXcOdon24BIfi0Eh26VOkfmj6pSxXDFkSVmStKZVP+bGo0T7BLXhinZWZU7qt7vauBkhoJhoUW/GIzUJXHj1NFy1Oj1E0Z6dda5xPn9g2PzksPMdpqe5m91MTfnPV4hoBxTtYAiaFwQ9E1PFqlAqtUv2B4l2W2TJZIT3+hVXwFP/8TkqyDqY13uTYCMU7S7wa1LKi4AHAS8RQlwEvAb4gpTyfOALzr8BHg2c7/x5AfCe09/kEVEsskxKEW0NI5lyor1fzLt50CV6Yjoq3vlO+O//hl27wr8PWUfm55mTFSql8EMWOlY1+FgjRLusURKcPM11iqGsAKH2pJ1UasSJ6T4IRcY9RVujJABUJvxFZ5BoAxRyShVfnFb31Ui0lxLaBP4AU1NCWUdOKKVhkCTkcpBNdZlhnHo7RTEe7cOg2tYnFilpDmrTUqdIb1YR+ng2/H1Cqe6OHFFEu2hQr3pJ3zqiIdpdknQXmtqASlCBKmEFMtxHwWws/W5/uHWkFzNmZgh/qL6PSyVBPV6hvhijylxksfNOYhbjMD/PPGXK+fDCWiiqIjEmpRMgl3dKsLvBUyZFmyz9jj515SiK9oRf84NCNkpcg58zv5igxEJkUJZKUMx2uJvdHPmp6qMd+8P97Kap69ed2AKdj9hRXYcRbTfmotuRRo82qAJUAMls9Oam08rG4gdymYn24iG10cxXokR7vl9UivbsrDoxC1wTDIY0kc4QMR1SsRFUjnJjoaVAd3RIGk4LBF2RpNtyxlN6iKLt+HIH5zBv3m70qM+qexfM0hH8Pn4MiX7cLpHxn2edcBE8NV3BOjKoDrsIWac0aimE7QXFuP40t5Zb4kSnQLOd0CrjJUd17bfMaSmzGWeNWe5qn9WQR5uY1p4oBJSSLZ9oZ80nVCZFG6CQVevQ/DG1Hg7mM4jHIRnr0lqK0ZxT3yk35vdBcGPt9aHmWc06hZR6y/r5CaCYbFNfTilFOx29plxRRdx60yfoEyc1EFPiZoIKK9rq3rn05glPUD9/PF1WO7PpaX66vD0UQOtazmi1YHraI9rBU+qJSfWG03MJ7/4CXHSR+ultrpdVqsGleC6kaAPcdtQ5ct6EAZGnnWhLKY9IKb/h/L0OfB/YCTwe+KBz2QcBpwt5PPCXUuGrQEUIsf00N3skyHxBmfRFVztDZZ3nqcy8sbz6qMhk4Moro7/3rCOJMr1DR2iRo1DUqz2HGhV6mlzD4DfPI9rbokeMxbI66l+gRCmljzzPZ3tK0TZMcKAmas+jndMkbQWq23ziPBj0B1DMK2/u4nG18BbGwteMRLR3xJR15ISyn+gm9bHCMrOM0ehmtEQ7uKiqXLfR9ygUBPVYxSfaA5kB1FGZ+j6dw9P0SJAbyLQSJGlLJ1Q7MtWs9pr2nEO0NZ5at9qfT7Sj6djyma5StIlpFe1QiW9iWtUp8qYaFIuwICq0ekmlEg+kHfFOYpZSMDeniHYxvIDkC0oVH1bJL5dXuZG9o/60/njXVcl039m1EYHZo+2qNKC3WSSTyvK0RIaZVo5xZrTEZ/fkMofYxeHD6jndfo7GOiLTARXZQAZX8mg7fv1uVw7dXLjPqtY64gRmmjZuwaDj5j2KaOcGSGe57Ae9to+coEOK4ljg+Q/0jxdPMYRod13v9GClS6+4lFnpDA5BSUwbAKrKyqf8zUVa78ttkmPpmJpbBucwb95o9KnPqc2jiWj7ef6HWEfc+697FF02F/zge4mQop3RE70gCSok9EJMtdDhBDWaywly6agaXSqJoVlHAPJZFQDdM8QWeOOl7uQf11gxACqppm8dyWoU7RGIdrGoTisXZjtO+6PXZJNdlpYFzZNqvAxmHQHHoz0k5302p07l+obNBUAx3aZO0SHamkwrFcGCKLM8q6ySqYH1QQh1b0NZRxwy/oQnwCc/qXzWAIfm1Zwr223+Z3oXl17qv4+XRrOxBCdPcjKjdtvBTcjElLqZ0wvpENG+/nrn+wbiD2i1WIrlPI+2i1gyDh/9KNzHz3qyWbChHm0hxF7gcuBrwKSU0tX8j4KTYkKR8LsD/+2Q87vB93qBEOJWIcSt024Z0NOMXqGMJEY6rj9K27vHyUFJY2Wifc01p9QGTxlJV2jdPQNAvhSeEfbvh4TocvPdv0ablNajnUioa9pkSNEmVY16x0vlGAuirIh2Rk+0c1nlnR5GwLIp36Mdz+uJduV8Xx4MetpcFEtqAmw4+YjzY+H38fxm7SSL/Uzody7K40kWKNOZUxOP7mh8vNJRJKGfpZCI5o8NEm3T5qJYhHq8Qn9OEe1YWqPQF5R1pOkqYBpPJzhE21W0B4i2pwKcbNMhGbHCAKQcT63vgYxeUyl0mWVMnUpoFC7POkLCWchWINpGRRvqFFSWFVoRoh0qsnD4sCLaA4pRPg+LImAd0XD6bCHGInmfmA5UbAwT7a52Qb3ED6w3WsWCirbueRfCL/E92y4ooq25bteOviLaMyli9EIE3m1vn7jKzUs0/zsEFG03YEwX3JxxMtAQ13q03aY1UYNPlzou46T3G2ajKBalUrSdeIp8VUO0OzlYWPCsYIWaf19CFglH3R3s57Ci7YxtTQEeUDnKez2h7Wc5wE90loJEAjoiTdeQRjD4rLaOqu+crWa01zSbqiQ5hHOqu9/nJBVfxR9yErMZiHZwfjUR7VqpxxxVmt0kOY0yXiyLsKKtmVvcAOhupz9U0W63pZqfDOtQOd32FW2N5WaQaOs24MWi2hjMOykAdRl6M4kerX7a8zUHbYEh68iQVJy5vK9o62obAJTSyyxQUvNpVh8wOS8qHtFOZjSiUM6vbbBMyhNr4nG48UbfUtpqKjX7DvZzopnlqqv89/Ces2YXTp5kPjMZuTf5Wpoci0zXlRXVxZOfrH6m0yCEpEUG5udZikUV7ZPzMXpP+YWBiXdzYMOIthCiAHwKeLmUciH4mpRSgmH0GCClfK+U8kop5ZUTG3Sj2zk1gaXyesXuNS9d5I95CTfx0eFEu9OBf/3XU2qDN2GnyjQPnVC/GyhKsGMHvOqiz3BPd4pZWTMe9WcSalEu0NCEGasHdUGUqVOklNEHxOTzwlO0TUpCNiN9RTuvD87Zdrm/txoMDAQolOLUKbI46+QjHg8vZIkEZGJtGu2kRxQG1xh3Q9KY0efDBRiv9rkLZYwvahaPYGCmmtQNRDtWoucUM9EdUxYK0BAlmg4ZGezDkErmBEOmq+Ev5BHt+WW6JLUKZCoTV8RoSM7Wc3e1+SEHVB9qTj+CivYwxcj/UDPRXugrop1hKfKMhHK/3nOPItqV8GepDUoxkPdYP1YWydPrupXXzF7Xfk8S0+wuhICjz/tNPsvPqIVbc4K1EtF2P6sVLzAjxxhLzGvfZ9fumCLazQqT8RmjMutG6evGnOcjNlTPA6VotXEKahk82oDnocyVDNYR0nSdlHq6QK6JCZgW2wJZF6L93Oym6Sy0vPziwcDAYECxW+p9sHpeOg3pZE8R7UU90fYU7Z6qKqizWbQG9tLxvJ5od+Mp/3lO6zfFTXIsHVUbZ6Oi3YT6gl/m2oV7gnKCmqfiD54+JRLqqD8U6KvLpLnWRFtD4kCNl1RMrQu1lME6UpPMMkZT5rSEvVSJKY+2Mz/phAs3ALrX0ael9KocO+KHUdHOtb2xbQrodsWnhNCLacWKWodcsqhVtNMqeLN1fMF7X68NwcJpbp5/jeCTzcXUGtPTx5AAFDMdT9HOZKJju1xWG1FP0dYR7bwfDKkUbUNGnZaA6Wn+iwcA8IAH+NeEiPb8PPNJNTGG7k0mQ4kFFlpJbyPzn/8J972vetmN5WqSg7k52kIFQy4PJLxxswxuNmwI0RZCJFEk+8NSyk87vz7mWkKcn8ed36sBrHMAACAASURBVN8DBFPm73J+t6nQ6cAD//bXAUiX9apseqLES3i3ihIeRrQTCXPllhXgTSrJih/VrPE0n1dVQTldksbiOS7R1gVpgUOMpJpUiln9xKNKCw9XtHOOTQLM1pFgYJwuUN6d4Bpz6snLT0TbW0i2abQTHqkf/EruvxdOuEFy0RszVpP8lL3q/VLRtFbucdtKinaD4gpEW9BIVWkeV5OgiWi3yLI03ybNEqIY3gx5KRqdjYWOaGfzqtiJdxytUewuPn+Z73KxUrSHWEdcNVR330IwWEdKJWj2MjQoDLeOUIZDh5inTKka/k75vPN93ZLZmqYUSsry1MWpempKB0ZmqOd8cqzLz/BPxo1DMBjSTLQFrUyVWcYYTy5or9m1P8URtnOQPexIzURe9/rZ4TJae0NS0CXpjTldP6YcG1GXhDantPs1v4OS888/P/oerkLvjW3Nxq1UEtQTNRYd51V+IL+yt6FaTvuKdmDjHEyB5hNtDZEoKKLtFc8ZGAvBDEG9vtDavJoD7rBEITr5JJPQjaW975zIG0g0OVrO82wqutVsCRbm1b0PEhGvGArjnudcd0KVSTlE27VFrZOiHRzOJqINkHdS/1VTDe3r1bEYR1BOUL3qqhTiYdlyXDFHBZ9H3yOUUpU4saR+ftpeblF3CLLu+RACJtOO3c9g1yiUY0rRdnzIeqKt+qjpxBOFagzEoVzshRVtXcGaQgyJmrtNWYCLua5vHdGIU249jfacakcqq7m3BZ9ot0lHVO9YDNLxLq3lGBw9yk9QKaAuvNC/xkujWW8r60hynGJxYGxmsxSps7CU8jYpQXseQD7tJE2Ym2NJZMlk4Kqr1Ge5KV5PnNDfi43GRmQdEcD7ge9LKd8eeOnvgV90/v6LwN8Ffv8sJ/vIg4D5gMVk0yCZhO/OKu9RqqQniyH3/yo92iYI4ahkiQItx7s7mJsX1JGQCxORSCfVNUXR0F5UKsGizLNEllJOT7Tzxbjv0TZMcNkcPtE2KNrBjx+sGAhOxopYiUWpZq3B42hwiPZy2lO0Bwm7e9S5cNJROnXWkQlB0yHqhnpC5LL4E79GPVHBkAWfjGgUyEIBGomKr75XDcFTZFmqLytiOuCFcb+fqxAkNRNpLucQo45T7ldHtC/sM0+FQ+zSTupBFWYk64hhZ+fez2kmyGjsGLWaOj48xiTcdZdStAfSZxUKakya8it715CnQ1LbnMGAVh1ZB/wbbFjpQvuJIYr28fgOOqQYS9W11+zZn0AS49+4jh3Zucjr7n07sexk7tHc/2RyQHXVdFE6Gw8o2maP9re5HwD3uTh6Y0olqPdy/iZGcwRfKsFCvOqP7Uoq8jqoDVX9LrVyBmNEgplwhhFtN0+wt4k0Kdrud9Z42weJts7alkhAJ5b2v3PKoPqJAktSfdfBOcwn41nmj6sTquDRejBHs5feT3Nvsxk3daXjxT8N1pGsIesI4OVIr83/VPt6bVtCnUBBpFYAqHswR3VooSU3LaiJaAdPwobNT/smfdXdtCZOZZ34Hc3ngLKONGLKTpmPt4zFfpbI0DyhNoCDXVArO0Tb7UOdR9s5TapTNLeloPziS2TI5qNju1pVQtv8rOq/QbUaAsHls7Msko/YUAFyaUdp/slPWKBENtMPTXdeGs25plK0Y9UQFVJfKEuJBerLGY9oD66v+XTPU7TdPNrlssrX/YhHqGss0fZxDfBM4HohxDedP48B3gI8QgjxI+Dhzr8BbgHuAH4M/Bnw4g1o80jIpdSDkc4bVuYgs1snog1OTtZ40V/IatHFIVgAQvcgg8pPCo6irUHwQSjl9ZHnuVLCzzpi8mjnhJ/ebwhJ+3V1YKBNNV4sQl2UaFAgyXIkVREoBbrRSbNInmyyE+FHnqLtCIs6D+r4pN8+XSYW8LNamFT8YhHqskCfcAR3qK0FqMfKKnsMkKtEc2SDax1Z1irAbv+4VhddlohcDpZJq/RL6O//xZep177DJdo8565FYpoJtZAZKj9+6Zc/xr/wMHX8o4FLsI6zTZV11hQD2rVtmTvZBz/+sWMdiZYNXpRZ+vVF+oYCI8WiCmwzBTWFvK7EjbndPcKyOEJ+fMPzXijAHb09AIxX9c/QIx+lPn+ZNDvyUdXb9Wy7ymAiF91kKjKY8cac1jqSi/sebU0qyKCiPc40Yzui36lcVn5xLwVaOfqwutYp95nPF8yBjI271cYiWL47mVQpMucCNgodSS6XVOW7XkvvV46k4tSQuEFVLVGMskEVDOmfFgyOOW9TnB/3nmfTJr9BgfkZ9XwEiXYyqTJwzDBOb06NAd1JmEe0Dd8ZGEgZMjyPtgnh9H765xmgm1DvX3vaz2hfD6aq1LgTqdWgTYZG29nEaISAfFmdUnZJGFP3CSF9om2Yn/Zt9+OMjES74GxyNCc+4K5DStEuG1LeZp1sXe6JziDRrlbczYU+uBb89aBBQWttAyiWYr51JBf9Qq4f/FjHsbzmop9TKMdpUEDOzLBInkIx+ozkXAJ8++3qlHGg6ql3orDQh9lZ5kUl6l3PZChSp97NeHPH4GmAm1iBuTnapENj0PO2W6KtIKX8ipRSSCnvJ6W8zPlzi5RyVkp5g5TyfCnlw6WUJ5zrpZTyJVLKc6WUl0gpbz3dbR4VY3m1Q01rBjUQJg7rSLTzeahT8on2WHQyDU5quipYoPKTAhTRH/sFH4RS0RAQU0mySJ6TVKK7WAfZXIyOQ/SGCSxvepPKRW8iT3VZUJOBppAMQCHdoUGeJjnymgh3d7GbX1D9pCOdY7v8p3uwKI73Po5dxqSeFApK9RumLpbLMC/Lvvo+UD0vZB2pd8iIKDG99looJZv8Bc8GIDFCgI+uLQfurwbLDBPaTUE2qybBGcaHLmQPvWyeh/ElleZBA3c8nWCMDNFAU4D9eyV3sJ/OnXfTJB+ZsAsFRaIbC3r/tXsN4BVOGKZoKwJmINouC+vrxz7AHz7yH/hLnmm0l+zbB99fVBuhsXM0TAPYvRu2J5ST7vkXfDnyuku0D7MD0BOwiI9Yp2jnE766q/nO7pT1HS7hPtyuJWl+H5rzESvLmT8/RYKSg0T7kDqqH8ycVKkITsbHfFuI5jlzPagmi1DIOmI4fXrjG+HGy3/i/dt4WhBLGT/HU6uzYyrfOVFFO2iHmZ/rkxDdyO0dG1OKtku0E7novJ3NOhvEvtC2JYJTXIdGJdquh7b20Eu0r1e3+19yohqdk91HzM1+FStoLIHVpHeCmNDEkAgBpWxHBVUSMyvau/3PN9kxJouKHU/k9WuMuw7NU6aU1s9zblrcGcYR9CPrYq3mBEMu6fP8gx9M26CgjZsB39/eIhuxKoE/5o7inMRrrSMxFmNF5xlJRDbF4Kca5HvfYyFei4gfIcvfXXdxsl+KEm3XOtLLsxCrhorUeG3J+oGZS9IS7bMWY1nH62RStINYR6I9Pg4z/cDR7Lhmcsr7D6dOuQXIOCmBikKvaIeJtr4tuXKSRQosUmBim2HnHQho27tX/z6gJkzTBFgoQKOfo06RvKFKZSGt8nUvktcG3niKdlc94VpFe7tPYoIFJULvkx/uSy8WYbGX9awLusWwVoPZbskYuBmyjnTjZGLRxS6Xgyfv/Tqf4XEAJDUL8yhEe2p3kiSu51PfhxNjfaaZUN/Z5NF2pY0ViDZAJm9YDM+Pcyf7vONFXdYRCGyWRiDaJqWzRXZoWkpvdh+Clz38+zyTvzJWKwv6nMfPr2qvAfjCxTfzJR7KFedEPdpuyjyXaA96hEGRwU7AR6xVtDNx36Ot8zw797pFjv3cod3xRoi2NgUa1GXeGCsRJNr1Iw3v/wRRrcJcbIyumyEoGx3bZTeQzqA0RxRtzbOay8ELH3/M+7epvH0n4H83nZA0MzWjoh0i2pQoJ5uRuNjxccGsGKfrWmG0KeicYkwG0h+BKVH2Cgjeh5GItuFRqU36/aY70XH/3wwq4EFn3cmX4rTJ0CatzaAFUM531ekGceP8FKyyvJKifb8ds9rXCwVY7Oc4SSVCOF1kC8pbPc0EY5nFyGfVxoUKem3p8/yDf0rdoGA8cSuPJeiRYIaJSNYq8O+tR7Rr0Y2+Csov+qdPmtNkNw6L225jPjkeUaJD2aKA6eVyNDFIPE5JNFQgaUp5uAeHZj7npBbtdi3RPpsxnlYPYSqvV69CWEeiPTEBMx3f3zuYSgpUQJgLo6KdcdMR6nfvwQfK5FcO7oC3TemH24H7jEa0h8G1A0wzQSGuV0PdojZNcuSzUaLtebQdEqdTZoMBboO5ul3kcsFMK9H3cO+V9zmaSX1sDFUxzUC0vRLI8Yryq8X1i91lk344g45oD/q4dRuZWAx2Oxk2TZP6xETAOmLYuHkM10C0g2Mos0+fKn//gSSH2al82kSJtkeilZWShGZsu9ccZYoYvQjpUWn35OiK9jC4DVzSb/6CRHviPE0uMAcX3vWPPJR/g8svj7zmLlrDFO18HhqyMFzRTgf8yhqi7RabAdiOPkzGnRPmqBo/p1SChU7OqGi7Cv0xJmksq/4btBVUKnBSVD2/ss4uU67FPXKla8soijZAesrfAOm+TyLhFIZyyO0gGU8m1e9aKd8KNqhoB+Mc5ilTTkXHy9gYzIht/sZB85yNTcS8VJym9q4FgiSopGmrC5doVw17yCABDwUPD7zuKdqa+ccdP3WKxu9bKvRXtI7s3uf/3vQ+hYSau/aMmRVtUM9iaVJvy8nkVVn0aSaYyEXfpzoWD/nSdeuzq1ArRVvf1uAmZhjRdufS5ET0yNlNM+vGUGmJdjGunuX5eRYS1cicPEi0jzWLkRSlAMXkEguUqCer2iDSfCnmEf4lmQ5RKPf5sUT7LMDY/dRC17juMStfvM5Ee7od8GhrAiEK23zmpluYAS7cqybQjNBPpCFFu2LwKwcI4rYd+gnuwvsFJoRTswx6E9xRprxI90EU3KI25LWBN56i7RBgraIdWAz2nKMnYLmcX7Ampjn2cwnLjzkP0JPbWg26/bg3CQ4S7VhMtWU6s1ulw0voA5LOn/I9vbv2npp1BGAPB9Xnmoj2ZCxAtPUbt3ujaGevuUJ7zb596ue/o/LMB8lf8D1cRVV3vO6StnvYSSnZ0op6yn7lZNTJG6wha0y0z7vCTLT5nd+BpzwFXvjCyEvpNFQyLV/RLkQ31tUqzPWKwxXtVCCPtiZuI3ivpziqbWbk/htsXq1einnKCPqRqXD7dojHJQfZ441LraJN1U8jeApEezAY0mQpyOzw2aBpg7LUH27LyeVgsZ/1rCOD81w8DuVSnxPUFNHW1CUYG4NZMear1ZoN4MRUjOkAGTedAK4Fbn/Fn/Ixnsp159xlvKbrTEtGRTvwe93jNGgd0VZjdJ7necrGwOVSScUwdEhFMhW5SNaKJHCCLg33rdNTL+yt6TMEueP0HnZS3qVXn7LFhKdoTxSj80JtPKasI22HaBejJ9Lu+BmmaAdjKDK56BeKWEc0aYnzeTVuXYKr89FnS0lvAzlPZaii3SPGTDPnncIFUcqqdIQLMQPRrqS8drRlKrRZTSTgt38brrsu+v82AyzRXkOM7VSDbbY7ZMF0YcqptwYYH4fpVsEPpNP4novb/SdGR0YAHn2tOpb9jryv9vXg4rdru/5oPLgDntipJ/QX3W/19yKoUuY1ARvgZCZxFW3NznxQ0U5oggfdCeKhfInshN5Tmw8EQ+rUE3cy+AI3AGZFG+AQuwD9BmRyEo4ldgwn2uf4SveVDzZbR7yCQadItMe3xX2ibbKOrKBoh6wjJf1Y2b9f/fwXVMkwl3i7cBcPb2HO6HOUg6M6GdS4yUnBMSaZYdwYpDiKdWQlou2mwbr5+u8gbrje/D4vfSl8/OPGDtpW9Im2bqNTrcJcZ2VFe5m0KjWuUXeDi+xKRHslRRuUkpbP9CMbnUQCdu4U3J3Yr4iE5tShUoGTsuSryBq7TKmsUsO5gb4m68gSKtOBbnEHSG/3+1k3bY+NwUwrZ1S0wfGLz6tARSGk1rJfrQlf0c5FxQJlCawN7cOJCcFxFNGOi57ZGfK2t8Fzn2t4cTQceP1NPPUZKeKvfuWK15oelaDSXfn5Rxr/n6doa6Z2zy5G2bislisxbkdVDNyx2zDJFYs8hU8AfkD8IF526b/yCt7GC6+5zfQWTlsqjG8zBP8XVIKAaSaYKEf7uVZTJyTuM6S1jgTmbdOJ29gu/6HRbWIGFW3dmCwUoCP9tmgV7XyMZlx98XlZHKpoTzOBlEJLtIv5Pm0yzDKmPSHP19K+ot1NRE6FXv96eNjDov9vM8AS7TXEzTerYjBPfOIIF5+iN24UTExAYzntqUo6op2d9FeVbdv1E8IN1ymS9pjYP2lfDy5MO6f0ZCS4OG/brVfxg964U4WvJOyicuk5+rY4+ZOb5MhpgjoGFe24JvBmxw74p/0v4hYeYzwPDSnaGtPgnj2wf7LB51A5iYYR7budFPK6PpycVJPkEhkvQ8wgztnrf0+dR35koi0OAXCyqd+UhawjJqvFvfFoGzJkusTaJdrnDHT1INHWKZ3+pmw7pZS+Ldu3KwVshnEmxgx5gnVl3wbhfikD0Z6YgIMH4R2fv2RVc8Jkue0r2hrbR60GXZkYaldyF9oWWW1wYRCrIdre6VPqHHKGTfHu3XAwsY86RQrJduTWVKsw1y8PJdrlsrKTuUfWg20pFJRF6ChTnKRCtWbIvlT0x7ye3MJSN2n8HHDV6An1rKaltqurVcGJ2IRDtKMb5+3boS6LPgHTkMqJCRVo2iJrTPsGwCteAe9/v/n1UVAqwYc+NNKGcxTrSOz8c42vD1O0XfJ3ghopw2FaqRpnzlkPd5xjPnH7E36Z3+L/8Mgo5wdUmfa38UpjNcxgYOPgaZuL2pjyYE8zwYRmE+/eKzdblE5Fdn/XJG8UP2q7fVa8Y4f+PRLxvq9oG4g2+GRcS7Rz0JJqwl7o5aPZQvLqhOprPJDtzryhtY4488LtS3u0NqJ8MeYEvcbo9uPGNWIzwhLtNcSBA3DPPWqRMOKXfmnd2+F6Nu+65umAXg2Njfsz3J5z9RNPeSpLgzy/nvx97evuA7WNY8bMC8FSrNt26VXKRAL+pPIa/uOmd2pfHwUhQr9LT+hL1ThNJwNKrqDJxzqoaE9pnnbgkWNfJ0cLUxoVl2j3iRmPIK++pMFxZ/IyWUdgONGemoKjHZXJIK2p6gWQGFOL/z7uMLYVVraOPDrzRQAu2Ke35UxMwBJZmuTNvtAViHbIo22YRKemIEOL40yyY7IbuW5wYR5GtAFKmiN6UKTmR5xPm4x20gdGO5d3Hz4D0QY1X6x2371zYpmZIWRkcAOia3qwxLquKmoQq/VoAxy9+AbymucQ1Gb0oNxNgwIFjRWsUlGLuqdWa2xe7j7IVO1PCNi1C77HRfSJUx3XD9ygtUVXa8mdb13CYlS9915J61FPJJPVf+daDeb6JUW0NbaD85TTzFNmTaQfFDEaSrRPE77wBXjxi80HuCtZBXM5yMSXvSJhOqLnW8F2US7qBYfSmN9x288xxFCVSpSo8zu8zuzsfPCD1c/73U/7cnDt364PM2F8XKUsnGGCiT3RG+DOYXdxDgXRGLo+ANq0lKD8+i50RFsIqJZ6QxVt934PI9rZLDT7GfoIFtrpiP4ghHrm/8EJygf01pGymgSPtatceaW+LYvkaaM6Zx3dt2sOS7RPN973PpDrOwG6k+3B6qWAgbQEntTd5xlYTT5PniYxQ87QahX+6PI/V2VXDVkV3KN+iObLDeKFc2/hwR95qfH1lRDkvLrdMsCYc5R3iF3kNeWjIx5tTcEa9UIi/FPzPl7BGsNbXHSx/3fdYuMq2j/dcx2xmH6Bn5yEY0tl6hQpXbgzegFApcI043yLS41thZWJ9tX5b3GSMp9893Ht68EociPRdmdpA9GOxfxbalqAhYD9+9R43Hde9P5HFG2N0hkm2vqNw9QUzDqZDiYmVzFNerkCzUR7LXDtpf5Zt25YuvfFy94wRNFWRHu4nWu1Hm2AI0fM6Tz37IG7O1PMU6aoSZNWrSq12iXRJrvGSm3ZvVt4BXhMRDs4f+qO4AeJtlHRrqdY2n2+cWxXq/ge7QuiLM0l2j/gAuP3CbZlMxDt66+Hd71rde9Ryy7RIUU6tqwVz4Pkr1LSCz7laoB07jI8z27HuAUbdHja0+DOO+GhD9W+vGeP/3cT0Q7OlRNX7Y28HiTaxbg+F3dwHMZM1pHANaa21KrqZA+Gb2KOsy307yByOWjGVOyTlEJrwSqXBW38B0mraFf9Ae3uZ4LI56FDynvmdW3ZrLBE+wyEp2jfpR4CrVoWmLH2XGBY7UaoGvbSCz7HORw0Em2AZ/FBo6K6Vti1y/97JHWQg7Edikk0yZPTEO1EAlLJ/tAjYABuukn9HPQtOFAe7byxZDnAhZf6BPBSDQd2u+fgQcFVV+n7cHJSpZI6nNhD5YBhd1GpMM6sMRf6qESbTIYyCxTK+guC99wo9Loz4yteYbjAv2TYsWDRCeQ9cCD6Wj4PiVjPWxh0gb5B5byc0meoCS5M41NDSGcmMzxVzvnnKy/Zhz5kvmYNcMOD/e8xTNE2qbvgE+06RW1OXYCJCUXeysxrX08mIZvujaZoHw0/t0Hs3AmdfoKfTj2IwvaouuturN2NwyhEWzcud+2Cw6hNam1Kr3QGlTOdEjeqoj07C62WeWxXq3Bi7HwWYvrUcINEW3dvg8WL1ivjyHpgmAtrsqTI5o70rDFweaX3ccdcjJ7RxoIQSgR785uHN3bI8x78/FGItm48Ba0jxYR+g55OQ96Z000F54IKte5zAMa2DVe9R7aOZGvM/9PXAH0fDP5O69EOEO0HPCD6uvvZ7sY4KOJtdliifQbCPeo+eHAIVw68ENzth1AsqhXi3e82f9irX61mFLcGqgYf/Pwu7vjDf1ih1atDcIdsItq17f4KZwqYzGX6Q32sAPzKr8DcXFi+CL6Hc2sXyzuM73HhVf52XDcxBZWb5z1P/x7uZLXUTVKprlC90ICRibar8hh8FMF7bqjNohiIlCo83IBRiPZNN8Fll8Eb3hB9TQio5Zd9AqaxFKTTEHdKjJcSqyTa8/Pwwx+aX08k4NOfRnsWuoa4z32TXMo3Ab11LZKP2BAMCTBHjVJNPxC+/nXB57mBYU6XUr7HiSGEPrjRuURfx8QbTz/t7qYwEZ3EIicXKxDtuKakPITvVXVKfxZ9b4m27juPj6spY3HRfFpTq8Hx2QT9vtC60vJ52J47OZJ1ZCsR7aNH4ac/Nb9+3pQilDtS+tzVoY2zIfuV65fuE1/P8KgQhllHXOjIovus9kgMTZ04JlQuO2Oe/wB0J6IA5+xTg6RU0ivEnnVEmFXvXA6arRjzOy/y3msQ7rM4MQGvepXedbl7TG2ofvUBX9GuiYNE+9yopX/TwhLtMxDuZDs/P8QDF5htjBNPPA4zM/CLv2j+sEsvhcOHzX4NgBtugJe9bGibV4ugWmW0juz2F2zTBiSfk55lwJQbHCGM/uzgezcWzR7tcy9QM999d5uVwde9TpFJ0+0PBtsYmzOkncG2rki0X/xiRZIN53VBom1SKUfBKET75pvhf/7HHAtRLXaGEjAhVJVQgFJCfzQbXCQndg9pTCplXsVOI0SpyDe5nDYpLXkdtI7oxmVwg1Qe13+n3bvhBv5laFvKgSqxuuEX3J/eV5/QyCMjMzP659B932GBXEGirasYCANEe0K/oQoS7WHWkUNFlULGpGj3+4pUmsb2SkonwH23HeeQE7eh+xx37psb8p03GyYnh09TB3apzfBE8qT29eB8b3qfZzwDnsDf8Eu871Sbea9hWoeC/awji0GRRWeb8q4T6n6YFO1R4AaXmwI3PUX7mieF/h1ENqvG9uHD6t86LWZuTv187nPhrW/Vc46Lts9xnAne/jC9KOcS7W9lH0w8bjxQ3pRYvxxzFhuGSkURpl5vuPvjgzzLyev63tPWttMBo3UkUNVRtzMHlKXEKQS3Akc1wv1/3a6ZuCYScOutsHev+cxUp9gGEVyMT5VoDxasOVUVLHjPVzMBjkK0V0K13Oe7h/cC5n7eUV5kvpU2Eu2gAD2+awuEtztsNIW+cFGQaCt1N7o4BwllqTpkaXjjG4cen2/bneGHd0EuJ8lrcviXy4ro9/tmoh0ktLrF3f0+d7Iv9O8ggn5xk185SHSqNb3iEHwmdBuUYlER/Zl6hokJ/Zhzv89dd5mVuODRvUkN/ZmLD/G5nx6ItMtFpQKZWJulfnpTeLTXAufvUxlY2n39mAyOj3JJ/52zWfgbnuT8y3BEuEZ4zGPgllvM++/gXKkbt9kspOJdlnsJShlz1c2xbXE4CpmcedL+gz8YHmjtEm1T2JinaB9zC3lFr3E5xh2OO1RH2p//fPj85+E3f9PcFuJxJpgxJlbwiPY5P8ee9pCT000Iq2ifgXCLmcDwqO5n8SFewJ+dnkadRhgV7cDibXJUFJyAzWTy1IvnuF5KGJ6Y4oorRqt5YsJIRHsFxppKQSwmPU+ziZiuhKDqaHDUjAR30VxNRHmtCnXH/mPig+dOquPoUtJQRbQA//aZOi978mFK41tgRjcev/gvx+OSFjnjWAkuXKac0gC89rXw9KcbX3YX2m2adJIuvvxlRUhWUrRhuKJ9B+rsXfc8u9fUKREX+sU7uKFawWVlhBD+OLvEkKXRfc6PHIGLL46+DsqX7sKkMD7uQTPe302nNTtrakybCvBsNbibjsWeeS6LO+p9+YDhKADgK1+Bf/zHtWyaFn/7t1Cvm19fKYhPCBjLqj4sDiHalQerE5RLLjP38803Dz9Mdsdtx/AxnqJ9TK0NurE9SLR1m8SXvhT+7u9W+O7uZGCYFNy16Qc/CK+xWwGWaJ+hcBcq0xEkAJ/6FHzsY6elPacDbiEYUzq2IIm87DL9Ne6CWKmcesq1YLW/9PmG5wAAGOJJREFU9fRJTkz4bTxV9V0IyKV7tMmQibVPOZI7eK+GprdcASvUtBkJtXG/MSZ1ff81Sj5cfsRjje/zkMcU+cNP7Dhtns5VYYVdoRCwfbv6Itt3GEqNBzY3o6QIN8Gdc0wnSwBXXw2f+Yx5Q3VvFW3d+M/l4EFTdwIY1d0guT7VTSYodxzoA3QhTJwf/nD9NUFF20S0D1zoTyimTfyOC51g7vTGW5rWAg+4eJFxpnn97j83XpPNqrGtCyL1cM018KhHrXXzIkgmhxNKdz4Z9sjuGVNCQNEgBADMnFAD4P73v9dN9D/HEUWW9cmXvA33woL5+XCf1U9/Wn33U92w8rjHKU/gM5+pfTlI4E2b1c0KS7TPULiL3NCd35OeBE996mlpz+nA3/89/Nd/mY+UgoTJkAbVu2+nPFkQJgmr8SuvhGQyvDEYCpN0COSKavGemFqbQKHV2D7cBaqhT5IyEi66WE1rgr5R6L3sSiUH9rJbKEfUMAihUo996lPGS9yxqMsuAGFyPVTRXgEuSVwNWQ9a3nSEvVBQAa11ShRF3Zin+QW/pRrz+BvNpNPdjK1m7LvpyEynaZde6s85pup1wX4x1oCp1XgjrwXMc9TOneqLbCUP6zBUKjDNNq6vfMN4jTvnnKrgcLpx223wk5+YX987oSxtJUN6P1A1OwAuv/zU27F/v6IBn/iE/vVy2SfYprH9sz+rhKsf/1g9+6taQy67zPgGF1zg/33IcrYpYT3aZyhcJXUrReauFuVyuEDOMJjUBHcyWc2EHZwnbrzx1N9nFExOqoCxoe1tNMwVI4BcTjV4Ylh2jRHgZsdaDd7wBpXE4zGPOfX3eOB1afhdlWfZhGc9S6W2fvazT/1zNh0+/OGhL+/eDV/9qtn/GyR6qyHarqK9VuUCdAlbhIBKsc/sfIyqOAnod1TPflGWi66Aq64yj4VvfQtOnBjehltvHX46+LSnKbuAKXA5FoOvfU2RK5NdLLi5MJKVapXX8mZecMGXmdjzb9pL3I3JqHPhpscDH6gKvb3mNcZL3Pl8NZu704mVFNntNZVtJB83Zx35wAfg7W+HCy889XYkEkP35p4t6rvfNXOJVAqe8xxlUzFZUNYCwSXMlK1os8IS7TMUC079irOJaI+CP/uz4QGi7iJ1qv5sF69+tfKhusEm64XJSTUJDl1gVjgT371bpdcaljhmFBw/PjSd+kg4cAC+/vXVvcdV16zsqY7H4UUvWt3nbDW4iraJaAftCmtBtA0xTfcaJsJYKUtm56HKHKD3KwmheNowlMsrE7Qrrhj+ejyuEvMMQyazBkfezuQ10dVX5gSVrxvMNpYth2RSFXobAlfRHqH0w5bAWFEx1say+STmIQ9Rf9Yb55yj1phheauvvVb9PKqvY7VmOPdctVm96KL1/Zy1hiXaZyjmnaxxw2ppnI0w5aR24RLt1e7M3/KW1f3/UbEWx/QHDqhNwTBP7Sgwlio/zSgW4df4fR4U+y/g4xvdnE0Dd/NosvaE0vutge1jrYi26bSmWhNwEKpybm0+aBPgHe9YIZuCu1v6rd8yXnLTTfCRjwwtbXDG4cUvhl/91RVikrYQxnao4IXZhMGsfxrhxlEME+3comvrXC6AL34RvvGNrVUVEizRPmPxxjeq4/GtFp270XBV3fU8AltLnHuuWlxWk+rIDd5cTaaPzYbf//erYedTNroZmwru0Wu3u/K1q1G0XbXpOc859fcA+P73h1eun5hSdpDqzlUeP20ivPzlK1yQy63oyXnc49QmZ0sE8a4Rbr4ZXvKSTZHSfk3wyJddAO+En3+zIWr/NMI9pRyWISseh9tvX3+xZffu1QXbbxRsMOQZiqc8RZX7PVOO0k4XXFV3FDKyGfCa16gA0NXATSt2Ul8PYmvi6qvPnGiwNcJznqNOuJ773JWvXQ3R3rFDLc7D6lyNggsuMGcHAnjw1Wr5kvdfwddxFuJsItmgvu+ZQrIBzj1PICU88rEb/6We9jT1cyX71IEDQ4J4z3JYom1hEYB7JLVVFO1cbnV5q8H33m1FpcBidOzdC3feOVrcwAppuVfEsPzxa4Xrr1c/v/WdMyNftIXFZsRTn6pOluzp+KnDEm0LiwBcH9rrXrex7TiduPpqVUzgTW/a6JZYbDTe8Q61oJ4OorxauEGSr371xrbDwuJMx5lkK9wICLlWOZg2Ea688kp56623bnQzLCwsLCwsLCwsznAIIb4updSGg24B3cLCwsLCwsLCwsJi68ESbQsLCwsLCwsLC4t1gCXaFhYWFhYWFhYWFusAS7QtLCwsLCwsLCws1gGWaFtYWFhYWFhYWFisAyzRtrCwsLCwsLCwsFgHWKJtYWFhYWFhYWFhsQ6wRNvCwsLCwsLCwsJiHWCJtoWFhYWFhYWFhcU6wBJtCwsLCwsLCwsLi3WAJdoWFhYWFhYWFhYW6wAhpdzoNqw5hBDTwF0b3Q6LkTEOzGx0IyzWHbafLe4N7Hg582H72GItsZHj6Rwp5YTuhTOSaFtsLQghbpVSXrnR7bBYX9h+trg3sOPlzIftY4u1xGYdT9Y6YmFhYWFhYWFhYbEOsETbwsLCwsLCwsLCYh1gibbFZsB7N7oBFqcFtp8t7g3seDnzYfvYYi2xKceT9WhbWFhYWFhYWFhYrAOsom1hYWFhYWFhYWGxDrBE28LCwsLCwsLCwmIdYIm2hYWFhYWFhYWFxTrAEm2LLQ0hRHKj22Cx/hBCPFQI8YyNbofF5oedE84O2DnBYq0ghLhOCHGtEGJdOLEl2hZbFkKIRwCvFkLs2+i2WKwfhBCPAt4B3Dnwe7ExLbLYrLBzwtkBOydYrBWEEA8FvgT8X+CB60G2LdG22JIQQjwQuAW4ArjRLqxnJoQQ1wGfAn5RSvnvQoiiEKIAIG3KJIsA7JxwdsDOCRZrBSFECjgPuBH4EPA64MFrTbYt0bbYqpDAs4A/AHYCPx9cWK2yccZgCTgK7BNCZIG/Bj4ohLhFCHEh2L628GDnhLMDdk6wWBNIKZeBvwM+J6V8N/AF4LXANUKI+Fp9js2jbbFlIYTISilbQogbgMcC08DHpJR3CiGEVTfODAghrgQ+AOwDXgP8BfBG4GIp5SM3sGkWmwx2Tjg7YOcEi9VACPEg4H7A14DDUsrpwGv/G3gY8DLgIUBVSvn2VX2enXcstgqEEI8EHgV8H/iGlPIbgdceATwa+AFwDlCTUr5oQxpqsSoIIe4DHJNSngz87kHAVVLKdwZ+dwvwPCnl4Q1opsUmgJ0Tzg7YOcFirSCEeCzwLuCzQB7oAe+SUt4auObZwG8DAnislPK21XymtY5YbAk4vrw/Bg4B24BPCiEe574upfwc8D7gmajj4z/biHZarA5On34feI0QYtz9vZTyq6j+d6+7CRgHWqe9kRabAnZOODtg5wSLNcZVwGudTfebgK8DbxBCXBG45iRQBR69WpINlmhbbB3sBf6flPIdUso3AzcDH3J2py7uA9wfeFRQ2bLYGhBClIGHA7+BOhJ+qRBizH1dSimFEHEhxDOB3wSeI6Wc25jWWmwC7MXOCWc07JxgsQ5IAf8LQEp5O/AxlLr9AiHEuBAiA1SAq6WU31uLD7RE22KrYBqouf+QUv4/lFL1diHEZc6vbweuXKuHw+L0Qko5D7xfSvkW4H8DV6IW1onAZUnnz41Syu9uQDMtNg/snHCGw84JFuuA3wPuI4R4JYCUcgaV3q+MspctAR9ay7FkPdoWWwJOGp7PA9+TUv5y4PdvcH731xvWOIt1gRBiD/Ae4FYp5euFEE8Eviul/OEGN81iE8DOCWcf7JxgsRoIIWJSyr7j73818FUp5Vud1z4M3CKl/PBaf65VtC02PZyHYxkV9HSdEOJPA+mbssDFG9c6i/WAECIhpTwIvAilPnweVaDCKgMWdk44C2HnBIt7A12KRyll3/n5VeDNwKOEEB8QQvw68CDgP9ajLZZoW2x6ODvQtJSyhfJb7gL+QgjxF8DjgI9uZPss1g7u5Cil7Do/DwLfBS5DRX//aAObZ7EJ4KTps3PCGQ53LrBzgsUpogJhwu0WohFC3BcV7PhzqKxEXeAJUso7Ne+zaiTW400tLE4VQoi9qDROrcDvhJSyLVSp1PNRD8fVwBTwJjvRbj0M6WcphLgaeIiU8q1CiClgB/Bw6788OyGEeAgqU8Ax4ItSysNCiLidE84sDOnnnp0TLO4NHEvRR4UQT5dSfspdW5wN+v8C3gm8VErZQHm21xVW0bbYNBBC/BxwB/AiIUTR/b1Dvu4PvB2YllL2pJRfllJ+wi6oWw8j9PM7gduc3x0FXiGl/OaGNNZiQ+GkdvtjFIF+MPBaIUTBIV92TjhDMEI/2znBYiQIIfajis28G3ivEOLJztri8t2LgNdLKb+ks5esS5tsMKTFZoCTsun3gBngQuBzwJ87O053Il6UUn7R3Z1uXGstThX3sp9jrqfO4uyDEOJcVMW/V0gp/9shXK8CXiKlnLVzwpmBe9nPdk6wGApHvLlBSvm3QohHA38NPFdK+cmB6+JSyt5paZOdmyw2A4QQSeBSKeWtQpXX/T3g06g0O/OB6xKuV89i68H2s8WocBbMnwM+6pIroSr/vcdJ5eded9oWTIu1h+1ni7WAk3M9JqWcC44VIcTPAB/HIdtCFab5kZRy4XS1zVpHLDYFpJQd4BvO329Fpd55EqqiG0KIRwshtlvytbVh+9liVEgp68BfO75KN55oGhW4hBDiWiHEuCVfWxu2ny1WCyHEk4FPAJ8VQvwSyh4CgJTyH4GfB/5ECPFBVKaazOlsnyXaFhsGh1T9UfBXzu9jUsr/RpGwRwghPg28C5W2y2KLwfazxajQjBU58PMocEII8QTgd4H06WyfxdrA9rPFWkEIsRN4HWodeQ1wCXCTEOI69xqHbH8SeCzwK1LK46ezjZZoW2wInCjy9wNXCyH+CsAJfIk7yoZwSNiPgWuBn5NS3rGBTbY4Bdh+thgVQ8ZKLKBmLgEfAF4LvEhKec/GtNbiVGH72WKNkQEWgW9LKb+ECoJsonJkXwAghLgKuC9wvZTy26e7gZZoW2wU8sBvA9cAaSHERyBEwqSzU90JPFJKedvGNdViFbD9bDEqTGOl73j7AZaBMeAmO1a2LGw/W6wZpJQ/Ab4JvFoIkZGqSugngBrwAOeyHwA3bgTJBhsMabGBEEKUpJQLQoga8CdAT0p5k/NaTUp5QgiRl1IubmxLLVYD288Wo2KFsZJBkbSqlPLHG9lOi9XB9rPFWsDNNiSEeBSqSuxtqKDalhDiESg7yROdOICNa6cl2habAUKIceA9wCzwVeA+qFyXyxvaMIs1he1ni1GhGSsXAr9hg+LOLNh+tri3CBBs92cKeCpwKco28n+AJwLPAH5eSrm0gc21RNvi9GGUXLdCiNuBbcBDN+qYx2J1sP1sMSrsWDk7YPvZYi3g1GFYCp5+CiFSUsploSqGXg48DhUQmQeeL6X8xsa01octwW6x7nCOB5eklM3A7xJSyq4QYjvQkFLWnQjzJHCttKV1txxsP1uMCjtWzg7YfrZYKwghngT8MpASQnwYFfz4NYdkPxy4Cfh1KeVnhRC7UGPr5Ea22YUNhrRYVwghHg98EPgrIcQzhSpSgjPRPgyVzm3cuXwZeLSdaLcebD9bjAo7Vs4O2H62WCsIIXag0jy+BngzMAE8RwhxgxAiB7wJ+Kybtk9KeWizkGyw1hGLdYQQ4gDwKZR3ahz4WWAXKv3OV4EvA/9XSvmpDWukxaph+9liVNixcnbA9rPFWkIIsQd4v5TyEc6/96OCHy9B5cf+oZTy0CgWpY2AtY5YrCfGgUNSyu8BCCGqwItRVZqmgcdLKY8JIQTAZnxALEaC7WeLUWHHytkB288WawYp5UEhxIIQ4vellK+UUt4hhPhnlKd/t5TyX5w87P2NbqsO1jpisZ64DVgQQvym8+/LgR+iihHslVIeAzXJ2ol2S8P2s8WosGPl7IDtZ4tVQQhxjRDiSUKIZzi/+i2gKIR4JXj5s29FVYHMbFaSDZZoW6wxhBC7hBBl559N4H3AlUKIvwcul1K+FPgG6uEQG9VOi9XB9rPFqLBj5eyA7WeLtYIQ4jHAe1HVHH9DCPFm53Tkb4BzhRB/6FxaADpAfGNaOhqsdcRizeBEjr8FeK8Q4kNSymngc0KIL6COeKadS4vASatkbE3YfrYYFXasnB2w/WyxVhBCnA/8DvBCKeVXhBB/CbxTCFEC/hW4G3idEOIWYDfwLLnJi53ZYEiLNYEQYgL4GHAQOAQcBz7mTLjB614OPAd4hpTyO6e9oRargu1ni1Fhx8rZAdvPFmsJh2hfLqX8uBAiDlSAzwDPk1LeFrhuCmhJKec3qKkjw1pHLNYK88DLUXkuvwmcB/yCEGIbqIIFznVx4Ol2ot2ysP1sMSrsWDk7YPvZYtUQQuwRQiSBn0opP+78ui+lnAV+DCw6110BIKU8uhVINlhF22KVcNLuHAUSA0UJbgQeCvxISvlOIcT95Sao0GRxarD9bDEq7Fg5O2D72WKtIIR4LPBW4D+BEvB6KeUPhBBJKWXHsYm8CrgMeD1w9eCJyWaGJdoWp4zAw/EfQBnn4Qi8fiNwKXA/4AbgAinlPRvRVotTh+1ni1Fhx8rZAdvPFmsB57RjF3AL8FLg+8AzgFcCD5dOASMhxAdQBHwK5d3eUoWNrHXE4l5DKOxGBb/8Cirtzn8DXxRCXOxe5xQj2I/ahV5tJ9qtBdvPFqPCjpWzA7afLdYSTlDsYZSS/SPguJTybajx9c9CiPs4l86hxtLzthrJBpt1xOIUIKWUQojBh+P3hRAd1MPxMCnlD4UQ24H7A0+wvrytB9vPFqPCjpWzA7afLdYKQojzgCpwB+pU5OlSyt8DkFL+oRAijUrt91zg88C7pJR3bFiDVwFrHbG4Vxh4ON4NfN19OJzXXwVcBLxIStkSQhSklI2Naa3FqcL2s8WosGPl7IDtZ4u1ghDiccCbUUr1d4B/Bv4I+FMp5e861+wFXiulfP4GNXPNYBVti5GheTg+DPyRECLuPhzAx4FfR1UAw060Ww+2ny1GhR0rZwdsP1usFcT/b+9uQqUq4ziOf3/hJasrSFAQLYKkFyLIEhRuGZLhoqjuoiDKhSSBy4rcZEVFIOGiRRG9bO4qjKzIgjJqY0WF6c0siCSKotyZKFRC9W8xR7qJ5rkvh+vMfD8w3DNznjnzMP/nOfzm3MM5yRiwBbirqiaTvAgsB8aAT5tL+m0FrgOuTnJuVR2cvx7PnkFbrUxzciyjd+3LX+erv5oZ66y2HCvDwTqrA09V1WSzvAmYqKpfkqwCHgYeAFYA6/o9ZIOnjqilZmd7aVVNNM/Pozc5bk5yMb3J8Qf/Tg7Py+tD1lltOVaGg3XWXGp+mJ1TVYeb5QuAt4CbqupAkouAn5s2fXGd7FMxaKuVYZwcw8g6qy3HynCwzupKkgXAQuDNqlqdZC2wErivqn6f397NHS/vp1aq6q+qOtw8DXAIONjsaNcCDwEj7mj7m3VWW46V4WCd1ZWq+rM5l/+nJJuB+4FnBylkg0e0NQtJJoADwBr8l+HAss5qy7EyHKyz5kJzw5oRejeqGQFWV9X++e3V3DNoa9qGZXIMO+usthwrw8E6qwtJ1gG7+vFmNG0YtDVjgz451GOd1ZZjZThYZ82lJKkBDqMGbc3YoE8O9VhnteVYGQ7WWWrPoC1JkiR1wKuOSJIkSR0waEuSJEkdMGhLkiRJHTBoS1KfS/JYkgf/Z/14kitabOc/7ZI8keTGueqnJA0bg7YkDb5x4JRB+/h2VfVoVb3fWa8kacAZtCWpDyXZlOTbJB8BlzWv3ZtkV5K9SV5LcnaSMeBWYEuSL5IsaR7vJtmd5MMkl5+k3USS25tt/5Bkc7Pu8yTXJNmR5LskG6b0a2PThy+TPD4PX40knTYWzHcHJEnTk2QZcCewlN5+fA+wG3i9ql5q2jwJrK+qZ5JsB96uqm3Nug+ADVW1P8kK4LmquuEE7Y7/6B+rammSp4EJ4FpgIfAV8HySNcAlwHIgwPYk11fVzs6+DEk6jRm0Jan/rATeqKrfAJqADHBlE7AXA6PAjuPfmGQUGANenRKkz2z5ucc+Zx8wWlVHgCNJjiZZDKxpHpNNu1F6wdugLWkoGbQlaXBMAONVtbe5TfaqE7Q5AzhUVUtnsP2jzd+/pywfe76A3lHszVX1wgy2LUkDx3O0Jan/7ATGk5yVZBFwS/P6IuBAkhHg7intjzTrqKrDwPdJ7oDe7bSTXHV8uxnaAdzTHDUnyYVJzp/F9iSprxm0JanPVNUe4BVgL/AOsKtZ9QjwGfAx8M2Ut2wFNiaZTLKEXghfn2Qv8DVw20naTbdf7wEvA58k2QdsY3bBXZL6WqpqvvsgSZIkDRyPaEuSJEkdMGhLkiRJHTBoS5IkSR0waEuSJEkdMGhLkiRJHTBoS5IkSR0waEuSJEkdMGhLkiRJHfgHH6sViIo9xSEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the predicted values and actual values\n",
    "plot_result(test_df, pred_df, dt_col=\"datetime\", value_col=\"AvgRate\", look_back=ts_pipeline.internal.config['past_seq_len'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate mean square error and the symetric mean absolute percentage error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:48:24.353282Z",
     "start_time": "2020-04-05T09:48:23.926760Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate: the mean square error is 3856.0159144400654\n",
      "Evaluate: the smape value is 7.833294706287807\n"
     ]
    }
   ],
   "source": [
    "mse, smape = ts_pipeline.evaluate(test_df, metrics=[\"mse\", \"smape\"])\n",
    "print(\"Evaluate: the mean square error is\", mse)\n",
    "print(\"Evaluate: the smape value is\", smape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can save the pipeline to file and reload it to do incremental fitting or others."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:48:30.202808Z",
     "start_time": "2020-04-05T09:48:30.023909Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline is saved in /tmp/saved_pipeline/my.ppl\n"
     ]
    }
   ],
   "source": [
    "# save pipeline file\n",
    "my_ppl_file_path = ts_pipeline.save(\"/tmp/saved_pipeline/my.ppl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can stop RayOnSpark after auto training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:48:33.286478Z",
     "start_time": "2020-04-05T09:48:32.916403Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stopping pgids: [9079, 9167]\n",
      "Stopping by pgid 9079\n",
      "Stopping by pgid 9167\n"
     ]
    }
   ],
   "source": [
    "# stop\n",
    "ray_ctx.stop()\n",
    "sc.stop()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we demonstrate how to do incremental fitting with your saved pipeline file."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First load saved pipeline file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:48:37.523732Z",
     "start_time": "2020-04-05T09:48:35.745574Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Restore pipeline from /tmp/saved_pipeline/my.ppl\n"
     ]
    }
   ],
   "source": [
    "# load file\n",
    "from zoo.zouwu.autots.forecast import TSPipeline\n",
    "loaded_ppl = TSPipeline.load(my_ppl_file_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then do incremental fitting with `TSPipeline.fit()`.We use validation data frame as additional data for demonstration. You can use your new data frame.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:50:10.820564Z",
     "start_time": "2020-04-05T09:50:09.321120Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 839 samples\n",
      "Epoch 1/2\n",
      "839/839 [==============================] - 1s 807us/sample - loss: 0.1133 - mean_squared_error: 0.1133\n",
      "Epoch 2/2\n",
      "839/839 [==============================] - 1s 818us/sample - loss: 0.1005 - mean_squared_error: 0.1005\n",
      "Fit done!\n"
     ]
    }
   ],
   "source": [
    "# we use validation data frame as additional data for demonstration. \n",
    "loaded_ppl.fit(val_df, epochs=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "predict and plot the result after incremental fitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:50:13.535064Z",
     "start_time": "2020-04-05T09:50:13.007472Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# predict results of test_df\n",
    "new_pred_df = loaded_ppl.predict(test_df)\n",
    "plot_result(test_df, new_pred_df, look_back=loaded_ppl.internal.config['past_seq_len'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate mean square error and the symetric mean absolute percentage error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:50:16.412656Z",
     "start_time": "2020-04-05T09:50:15.986206Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate: the mean square error is 3488.6956947005465\n",
      "Evaluate: the smape value is 7.372629462450104\n"
     ]
    }
   ],
   "source": [
    "# evaluate test_df\n",
    "mse, smape = loaded_ppl.evaluate(test_df, metrics=[\"mse\", \"smape\"])\n",
    "print(\"Evaluate: the mean square error is\", mse)\n",
    "print(\"Evaluate: the smape value is\", smape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": true,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
